{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the note book for Kaggle Competition - Give Me Some Credit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import xgboost as xgb\n",
    "import matplotlib.pyplot as plt\n",
    "import graphviz\n",
    "from sklearn import preprocessing,model_selection\n",
    "import itertools\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training dataset shape is (150000, 12)\n",
      "testing dataset shape is (101503, 12)\n"
     ]
    }
   ],
   "source": [
    "fileaddress = 'E:\\\\Kaggle\\\\Give Me Some Credit\\\\data'\n",
    "train_df = pd.read_csv(fileaddress+'\\\\cs-training.csv')\n",
    "test_df = pd.read_csv(fileaddress+'\\\\cs-test.csv')\n",
    "print (\"training dataset shape is {}\".format(train_df.shape))\n",
    "print (\"testing dataset shape is {}\".format(test_df.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "col_names = train_df.columns.values\n",
    "col_names[0] = 'ID' ## rename first column to ID\n",
    "train_df.columns = col_names ## assign new column name to training dataset\n",
    "test_df.columns = col_names ## assign new column name to testing dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Take a peek at training dataset\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0   1                 1                              0.766127   45   \n",
       "1   2                 0                              0.957151   40   \n",
       "2   3                 0                              0.658180   38   \n",
       "3   4                 0                              0.233810   30   \n",
       "4   5                 0                              0.907239   49   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                     2   0.802982         9120.0   \n",
       "1                                     0   0.121876         2600.0   \n",
       "2                                     1   0.085113         3042.0   \n",
       "3                                     0   0.036050         3300.0   \n",
       "4                                     1   0.024926        63588.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                               13                        0   \n",
       "1                                4                        0   \n",
       "2                                2                        1   \n",
       "3                                5                        0   \n",
       "4                                7                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             6                                     0   \n",
       "1                             0                                     0   \n",
       "2                             0                                     0   \n",
       "3                             0                                     0   \n",
       "4                             1                                     0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "0                 2.0  \n",
       "1                 1.0  \n",
       "2                 0.0  \n",
       "3                 0.0  \n",
       "4                 0.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (\"Take a peek at training dataset\")\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Take a peek at testing dataset\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "   ID  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0   1               NaN                              0.885519   43   \n",
       "1   2               NaN                              0.463295   57   \n",
       "2   3               NaN                              0.043275   59   \n",
       "3   4               NaN                              0.280308   38   \n",
       "4   5               NaN                              1.000000   27   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                     0   0.177513         5700.0   \n",
       "1                                     0   0.527237         9141.0   \n",
       "2                                     0   0.687648         5083.0   \n",
       "3                                     1   0.925961         3200.0   \n",
       "4                                     0   0.019917         3865.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                                4                        0   \n",
       "1                               15                        0   \n",
       "2                               12                        0   \n",
       "3                                7                        0   \n",
       "4                                4                        0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             0                                     0   \n",
       "1                             4                                     0   \n",
       "2                             1                                     0   \n",
       "3                             2                                     0   \n",
       "4                             0                                     0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "0                 0.0  \n",
       "1                 2.0  \n",
       "2                 2.0  \n",
       "3                 0.0  \n",
       "4                 1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (\"Take a peek at testing dataset\")\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check column type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                                        int64\n",
      "SeriousDlqin2yrs                          int64\n",
      "RevolvingUtilizationOfUnsecuredLines    float64\n",
      "age                                       int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse      int64\n",
      "DebtRatio                               float64\n",
      "MonthlyIncome                           float64\n",
      "NumberOfOpenCreditLinesAndLoans           int64\n",
      "NumberOfTimes90DaysLate                   int64\n",
      "NumberRealEstateLoansOrLines              int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse      int64\n",
      "NumberOfDependents                      float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(train_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                                        int64\n",
      "SeriousDlqin2yrs                        float64\n",
      "RevolvingUtilizationOfUnsecuredLines    float64\n",
      "age                                       int64\n",
      "NumberOfTime30-59DaysPastDueNotWorse      int64\n",
      "DebtRatio                               float64\n",
      "MonthlyIncome                           float64\n",
      "NumberOfOpenCreditLinesAndLoans           int64\n",
      "NumberOfTimes90DaysLate                   int64\n",
      "NumberRealEstateLoansOrLines              int64\n",
      "NumberOfTime60-89DaysPastDueNotWorse      int64\n",
      "NumberOfDependents                      float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(test_df.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check distribution of each features to see outlier\n",
    "\n",
    "* \"MonthlyIncome\" and \"NumberOfDependents\" are removed here as they have nan values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6e146470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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N35A0KU/TRRJmZtzxxYkcPFrL/3thLalJIb40dWjzDSVu6BqCfIq7s6JkL8MH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\nnoGqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6e536eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6e8a8c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6e64af60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VLH11d6pDE4mUYT3i//MbNcw9YTT5OcO6m2klJzNGfVMbk0vyuWr+Cdzz/Jtc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\nP2W4/r6HAkqdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6e8cd978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6e5ad438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pz6Gd2w4zW29ma8xsVbyt4s+vIsO9xCURKs1yYEnRtpuBh9x9AfBQ/L4S9QMf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6e5e3898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6eafcf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# remove ID, target variable Dlqin2yrs and variables with missing values\n",
    "feature_list=list(train_df.columns.values)\n",
    "remove_list = ['ID','SeriousDlqin2yrs','MonthlyIncome','NumberOfDependents']\n",
    "for each in remove_list:\n",
    "    feature_list.remove(each)\n",
    "\n",
    "for each in feature_list:\n",
    "    sns.distplot(train_df[each])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution of following features are highly skewed.\n",
    "\n",
    "* RevolvingUtilizationOfUnsecuredLines\n",
    "* NumberOfTime30-59DaysPastDueNotWorse\n",
    "* DebtRatio\n",
    "* NumberOfTimes30DaysLate\n",
    "* NumberRealEstateLoansOrLines\n",
    "* NumberOfTime60-89DaysPastDueNotWorse\n",
    "\n",
    "Take a log transformation to see if distribution can be less skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ID' 'SeriousDlqin2yrs' 'RevolvingUtilizationOfUnsecuredLines' 'age'\n",
      " 'NumberOfTime30-59DaysPastDueNotWorse' 'DebtRatio' 'MonthlyIncome'\n",
      " 'NumberOfOpenCreditLinesAndLoans' 'NumberOfTimes90DaysLate'\n",
      " 'NumberRealEstateLoansOrLines' 'NumberOfTime60-89DaysPastDueNotWorse'\n",
      " 'NumberOfDependents']\n"
     ]
    }
   ],
   "source": [
    "print (train_df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "log_trans_list = train_df.columns.values[[2,4,5,8,9,10]]\n",
    "log_trans_list\n",
    "for each in log_trans_list:\n",
    "    train_df[each] = np.log(1+train_df[each].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution after log transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6ec0d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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N35A0KU/TRRJmZtzxxYkcPFrL/3thLalJIb40dWjzDSVu6BqCfIq7s6JkL8MH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\nnoGqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6eafe828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3mS64nCu4nqPn36R3gDnRP+/n3f3hwjss2iC3o+CmhUQjscJphhGNlpYCo/ey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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6ea0cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R113gZYs76cxl2DtS5MBoCbPoP2c2/ijkMvQtKEz+AAVhNKo+lc8XuMej/yB+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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6ea1c780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VLH11d6pDE4mUYT3i//MbNcw9YTT5OcO6m2klJzNGfVMbk0vyuWr+Cdzz/Jtc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\nP2W4/r6HAkqdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6ec21dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6e92b8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5pHKF0mYureQmuFcCOFj28yEAlza7IZGgHz/91JtcjUZoxOc2nYOJZA5f27IP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\n3EREhmFwExEZ5v8DV9Abz9vqUhEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6eab7f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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92m1VlQTtrSUqUfzxVgIqQ8K3uq2SNBDu1ezuK1cG9qm9pURri6ik9UTEsPs83H6UpO3e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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6e6eb7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for each in feature_list:\n",
    "    sns.distplot(train_df[each])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The distribution after transformation is much less skewed. We may able to put them into machine learning algorithm later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove nan values in \"MonthlyIncome\" and \"NumberOfDependents\" to check their distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dSf8stf1Ims9eFc8zuk/Sx1ObbklTU7ufkfS/0g1bvynp5y7Xh23XoFy/XPfL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\nWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6e689940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cdNrJ5DLGL7fvrzlGRCa3RIHfzFaa2RYz22Zm11XZb2b2nXD/BjM7P+nYZuo8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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6e928710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "partial_train_df = train_df[['MonthlyIncome','NumberOfDependents']]\n",
    "#partial_train_df.dropna(how='any')\n",
    "partial_train_df = partial_train_df.dropna(how='any')\n",
    "\n",
    "sns.distplot(partial_train_df['MonthlyIncome'])\n",
    "plt.show()\n",
    "sns.distplot(partial_train_df['NumberOfDependents'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "monthlyIncome is highly skewed. let us take log transformation on both then check their distribution again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XOeIx4/WrFrNp5SKeP3Ka7+07xaM7BgAYHhnjr594mY+8YyNLG2sZyORYVJdg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6e1c2240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tAbr2/1iwAXV/rjHBzH5nZpvNbIuZ3RkbIx9//IHYVOvbYt9vW2xVuE1mdr+Z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6eba3908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "partial_train_df['MonthlyIncome'] = np.log(1+partial_train_df['MonthlyIncome'].values)\n",
    "partial_train_df['NumberOfDependents'] = np.log(1+partial_train_df['NumberOfDependents'].values)\n",
    "sns.distplot(partial_train_df['MonthlyIncome'])\n",
    "plt.show()\n",
    "sns.distplot(partial_train_df['NumberOfDependents'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Post transformation looks better than before. I will keep log transformation on both at this time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df['MonthlyIncome'] = np.log(1+train_df['MonthlyIncome'].values)\n",
    "train_df['NumberOfDependents'] = np.log(1+train_df['NumberOfDependents'].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "check nan values in training set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                                          0\n",
      "SeriousDlqin2yrs                            0\n",
      "RevolvingUtilizationOfUnsecuredLines        0\n",
      "age                                         0\n",
      "NumberOfTime30-59DaysPastDueNotWorse        0\n",
      "DebtRatio                                   0\n",
      "MonthlyIncome                           29731\n",
      "NumberOfOpenCreditLinesAndLoans             0\n",
      "NumberOfTimes90DaysLate                     0\n",
      "NumberRealEstateLoansOrLines                0\n",
      "NumberOfTime60-89DaysPastDueNotWorse        0\n",
      "NumberOfDependents                       3924\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print (pd.isnull(train_df).sum(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check nan values in testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                                           0\n",
      "SeriousDlqin2yrs                        101503\n",
      "RevolvingUtilizationOfUnsecuredLines         0\n",
      "age                                          0\n",
      "NumberOfTime30-59DaysPastDueNotWorse         0\n",
      "DebtRatio                                    0\n",
      "MonthlyIncome                            20103\n",
      "NumberOfOpenCreditLinesAndLoans              0\n",
      "NumberOfTimes90DaysLate                      0\n",
      "NumberRealEstateLoansOrLines                 0\n",
      "NumberOfTime60-89DaysPastDueNotWorse         0\n",
      "NumberOfDependents                        2626\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print (pd.isnull(test_df).sum(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the feature \"Monthly Income\" and \"Number of Dependents\" have many nan values in it. We create new features to see if observations with nan values in these two features are indictive for serious dlinquency rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MonthlyIncome_Null</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>0.069486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>0.056137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MonthlyIncome_Null  SeriousDlqin2yrs\n",
       "0               False          0.069486\n",
       "1                True          0.056137"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['MonthlyIncome_Null'] = pd.isnull(train_df['MonthlyIncome'])\n",
    "grouped_df = train_df.groupby('MonthlyIncome_Null')\n",
    "Dlqin = grouped_df['SeriousDlqin2yrs'].aggregate(np.mean).reset_index()\n",
    "Dlqin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NoD_Null</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>0.067410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>0.045617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NoD_Null  SeriousDlqin2yrs\n",
       "0     False          0.067410\n",
       "1      True          0.045617"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['NoD_Null'] = pd.isnull(train_df['NumberOfDependents'])\n",
    "grouped_df = train_df.groupby('NoD_Null')\n",
    "Dlqin = grouped_df['SeriousDlqin2yrs'].aggregate(np.mean).reset_index()\n",
    "Dlqin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems observations with nan values in \"Monthly Income\" or/and \"Number of Dpendents\" have lower deliquency rate than those with valid values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150000, 14) <class 'pandas.core.frame.DataFrame'>\n",
      "(146076, 14)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ID                                          0\n",
       "SeriousDlqin2yrs                            0\n",
       "RevolvingUtilizationOfUnsecuredLines        0\n",
       "age                                         0\n",
       "NumberOfTime30-59DaysPastDueNotWorse        0\n",
       "DebtRatio                                   0\n",
       "MonthlyIncome                           25807\n",
       "NumberOfOpenCreditLinesAndLoans             0\n",
       "NumberOfTimes90DaysLate                     0\n",
       "NumberRealEstateLoansOrLines                0\n",
       "NumberOfTime60-89DaysPastDueNotWorse        0\n",
       "NumberOfDependents                          0\n",
       "MonthlyIncome_Null                          0\n",
       "NoD_Null                                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train_df.shape,type(train_df))\n",
    "train_df.dropna(axis=0,how='any',subset=['NumberOfDependents'],inplace=True)\n",
    "train_df.reset_index()\n",
    "print(train_df.shape)\n",
    "pd.isnull(train_df).sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple way to get some new features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#print(set(train_df['NumberOfDependents']))\n",
    "#print(set(train_df['NumberOfDependents']+1))\n",
    "\n",
    "train_df['IncomePerPerson'] = train_df['MonthlyIncome']/(train_df['NumberOfDependents']+1)\n",
    "test_df['IncomePerPerson'] = test_df['MonthlyIncome']/(test_df['NumberOfDependents']+1)\n",
    "train_df['NumOfPastDue'] = train_df['NumberOfTimes90DaysLate']+train_df['NumberOfTime60-89DaysPastDueNotWorse'] +train_df['NumberOfTime30-59DaysPastDueNotWorse']\n",
    "test_df['NumOfPastDue'] = test_df['NumberOfTimes90DaysLate']+test_df['NumberOfTime60-89DaysPastDueNotWorse'] +test_df['NumberOfTime30-59DaysPastDueNotWorse']\n",
    "train_df['MonthlyDebt'] = train_df['DebtRatio']*train_df['MonthlyIncome']\n",
    "test_df['MonthlyDebt'] = test_df['DebtRatio']*test_df['MonthlyIncome']\n",
    "train_df['NumOfOpenCreditLines'] = train_df['NumberOfOpenCreditLinesAndLoans']-train_df['NumberRealEstateLoansOrLines']\n",
    "test_df['NumOfOpenCreditLines'] = test_df['NumberOfOpenCreditLinesAndLoans']-test_df['NumberRealEstateLoansOrLines']\n",
    "train_df['MonthlyBalance'] = train_df['MonthlyIncome']-train_df['MonthlyDebt']\n",
    "test_df['MonthlyBalance'] = test_df['MonthlyIncome']-test_df['MonthlyDebt']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Group the data by age to see if there is any pattern in deliquency rate with respect to age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    age      mean  count\n",
      "0     0  0.000000      1\n",
      "1    21  0.078947    152\n",
      "2    22  0.073232    396\n",
      "3    23  0.113523    599\n",
      "4    24  0.119737    760\n",
      "5    25  0.122112    909\n",
      "6    26  0.124564   1148\n",
      "7    27  0.126365   1282\n",
      "8    28  0.131579   1520\n",
      "9    29  0.106434   1663\n",
      "10   30  0.109062   1898\n",
      "11   31  0.106647   2016\n",
      "12   32  0.113636   2024\n",
      "13   33  0.109009   2220\n",
      "14   34  0.097975   2123\n",
      "15   35  0.107949   2214\n",
      "16   36  0.099872   2343\n",
      "17   37  0.090800   2489\n",
      "18   38  0.089650   2599\n",
      "19   39  0.093761   2965\n",
      "20   40  0.085322   3059\n",
      "21   41  0.094376   3094\n",
      "22   42  0.093259   3056\n",
      "23   43  0.085714   3185\n",
      "24   44  0.073916   3274\n",
      "25   45  0.081556   3470\n",
      "26   46  0.086885   3660\n",
      "27   47  0.082539   3671\n",
      "28   48  0.075916   3741\n",
      "29   49  0.081703   3782\n",
      "..  ...       ...    ...\n",
      "54   74  0.028281   1379\n",
      "55   75  0.017979   1168\n",
      "56   76  0.017953   1114\n",
      "57   77  0.017442   1032\n",
      "58   78  0.023232    990\n",
      "59   79  0.024044    915\n",
      "60   80  0.021898    822\n",
      "61   81  0.012640    712\n",
      "62   82  0.031773    598\n",
      "63   83  0.021598    463\n",
      "64   84  0.018223    439\n",
      "65   85  0.020690    435\n",
      "66   86  0.013624    367\n",
      "67   87  0.022581    310\n",
      "68   88  0.030189    265\n",
      "69   89  0.037190    242\n",
      "70   90  0.017964    167\n",
      "71   91  0.038760    129\n",
      "72   92  0.000000     82\n",
      "73   93  0.014286     70\n",
      "74   94  0.028571     35\n",
      "75   95  0.000000     37\n",
      "76   96  0.000000     14\n",
      "77   97  0.000000     12\n",
      "78   98  0.000000      5\n",
      "79   99  0.200000      5\n",
      "80  101  0.333333      3\n",
      "81  102  0.000000      3\n",
      "82  103  0.000000      3\n",
      "83  107  0.000000      1\n",
      "\n",
      "[84 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
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XLxh/PR/BZWN7vpFiW/8q1Ry2rWvlP/+zN3BhuNxYdHj41CAvXhjBj36vF7UH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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6eb98f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped_df = train_df.groupby('age')\n",
    "dlinq_age = grouped_df['SeriousDlqin2yrs'].aggregate([np.mean,'count']).reset_index()\n",
    "print(dlinq_age)\n",
    "dlinq_age.columns =['age','DlqinFreq','count']\n",
    "sns.regplot(x='age',y='DlqinFreq',data=dlinq_age)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot above, we can see:\n",
    "* DlinFreq is negatively associated with age in general\n",
    "* age of 0,99 and 101 looks like outliers\n",
    "* DlinFreq looks like a quardratic function of age. Put a higher order of age maybe helpful"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove outlier in age and create new feature $age^2$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pGgIeFrfUFbpGNgS9NAW9WBpYlBpTRVNeuc6Ifw1cZ4x5AVgsIu+b6ouLyMUi\n8qqI7BCRr5Z5XkTke7nnXxSR04qeaxKRn4nIdhF5RUTOmur9qGNX0GezqCnI/MYA/lw9Nr/X5osX\nLGduvZ+GgAeBXK/7DLv7Ytz30j7STpaBWIqu/hiDsTSzKW9LqYmqdA3lR0AKyP/S3gN8cyovLCI2\n8H3gEmAlcJWIrCw57RJgee7POuCmouduBO43xqwATgZemcr9qNkh5POMCCxrlrXwlxe+lWWtYVrD\nPlrDPmwRBuNpvvP711h7+2Yeea2XjJPl0HCSrr44Q4l0rX8MpY5Kla6hvMUY8xERuQrAGBPLteed\nijXADmNMJ0Cub/xlwLaicy4D7jTux8KNuVHJAtx2wOcBn8rdTwo34ClVkZDPQ8jnIZ5yOO9tc0cU\noCzuHNnVH+cffrONt84L85lzl7LquGZ6h7IMxtO01PlGFJ1Uarar9P+GlIgEySU3ishbgKmuoSwC\nuooedwNnVHDOItw+8r3Aj0TkZOAZ4HpjzPAU70nNMkGfTdAXJJF26BtO8eirvWzY3MW+SJy3zqvH\nEnhl/xCvHYjylZ+/xCkdTfzFO5fy9gUN7B9MEPTZtNT58Hvs8V9MqWNcpVNe/wu4H+gQkbuBPwB/\nM213NT4PcBpwkzHmVGAYOGwNBkBE1onIFhHZ0tvbeyTvUc0gAa/Na/uH+LeHd9AfS9IQ8BBLueXx\nP3feMk5ubwTg+a4BPv+T5/j7X7/MrkPDxFMOe/rj9AwlyJQUrlRqthl3hJKb2toOfAg4E3fb8PXG\nmINTfO09QEfR4/bcsUrOMUC3Mebp3PGfMUpAMcasB9aDm9g4xXtWx7BbHu3E77EI+TxkswZLLGKp\nDE919vHdK05my+5+fvCYmxz5xI5DPPWnQ1y0ch5Xn72E+QQYTjo0BDw0h3y6I0zNSuMGFGOMEZH7\njDHvAH5XxdfeDCzPJUnuAa4EPlpyzr3Adbn1lTOAQWPMPgAR6RKRtxljXgUuZOTai1IT1tUfoyno\nViG2LMFnCbZ4ODAUR0RYvaSF049r5tHXerntiV1098d5YOsBHtrew/tPXsjHzliMMYZoMkNT0EdD\n0G0VrdRsUekayrMistoYs7laL2yMyYjIdcADgA3cZozZKiLX5p6/GbgPuBTYgbsQ/+mib/EF4G4R\n8QGdJc8pNWHlOnImnSxL5oSZ3xigb9gtm/9mcuR+7nhqN4eiKX7x7B7+86X9XH56O5evasfJGgbj\naZrqvNR1gtYOAAAgAElEQVT7NbCo2aHSWl7bgeOB3bjrFYI7eDlpem+vurSWlxrLw9t7+Ma9W/Ha\nUmi+NRhPMzfsZyiZoaM5xNVnHceJ7Y2FfizJtMOvnt/LPZveIJLIANAQ8PDRMxbzwVMW4fNYeG2L\nppC30NNeqZmm2sUhjyt33BizexL3VjMaUNR4ijty1vnsUbs7rl7aQn9Ro69C58hnukmky3eO9NoW\nzXU+wn7daqxmlqoEFBH5kDHmF7mvm40x/VW8xyNOA4qaiKvWbzxsCqy0u2Npa+JynSPbm4OsPWcp\n5721FUsEn8fSHBY1o1Sr2vDXi77+w9RuSamZpas/RtA7Mr+ktLtj2O+hoyXE3Ho/Xtsa0Tnyz3Kd\nI7tznSP/x93PsnlXH8m0w/7BBHsH4iPK7Cs10433EUlG+VqpY165Rfp42qG9OQS8OT3W1R+joznE\nuncuZdXSFgZiaRY0BvnqJSv4yOqOQufIcsmRewfi1PndrcbFLY2VmonGm/Lajtud0cLt1vhRigKL\nMebZ6b7BatIpLzUR5Rbp82sowKjPnb+ijf98aR/rH+1k72CcBQ1BzlrWwpOdh0Z0jjzn+DmsPWcp\nS1vrEBHCfg/NIS8eWwOLOrpUaw3lj2Nca4wxF0zm5mpFA4qaqOJF+vai9sBjra9cc96yQrDx2RbD\nqQxpx/DFdx+PZUshORLAEt5MjmwIYIlouXx11KnqLq9jhQYUVS3nfvshmoLeEfklxri5J+1lpsqi\niTRNIR//fMXJZI0ZkRwJ4LWF95+0kI+dubjQ1EuTI9XRotKAUvE2ExE5G1hSfI0x5s5J3Z1SM9xY\n6yvFGfd5dX4PvUMJWuv9DAyny3aO/MVze7jv5X1cfno7V6zqKCRHNtdpDouaGSoKKCLyY+AtwPNA\nfluKATSgqFmjeBE+7LOJxN2+KMVrKNect4xbHu0sG2w6WupoCLiZ85F4hoF4qtA5sjg58scb3+DX\nz+8tJEdmslkGYm65/DrNYVFHsUr/61wFrDSzaX5MqSLFC/RNQS/xtIMBfLZVmObKr6+Au2AfS2UO\nCzYAIkJjyEt9wMNAPE0knuYjqzt470kLCsmRkUSGmx/p5OfP7CkkRx6IJPB7bVpCPoI+LZevjj6V\nZsr/FPhivjDjTKVrKGqyxktyLN1CfNayFp7q7Ctk3ItIoXxLceABcLKG/liKoUQGYwx9wynu2rib\n3764r5Ac2dEc5NNFyZEhn4fmOq/2YVFHRLVLr/wROAXYRFFjLWPMB6Zyk0eaBhQ1WWMtwv/jZSdO\nentxsbSTpT+WIpqrCVbcOTL/f2lx58jCVuM6H17daqymUbUDyrvKHTfGPDKJe6sZDShqssYaoQCT\nei5fvqVUMuMwEEsznHQDy86Dw4XkyLzi5EjNYVHTTbcNl6EBRU3WWEmOX//1y6OOXgyM+txjXxk7\njSuRduiPpYin3H0wW/cO8sPHd/J818jkyM+cu5Qlc9zkyPqAh6agBhZVXVWp5SUij+f+HhKRSNGf\nIRGJVOtmlTranb+ijRs+cAJt9QEG42na6gOFaauO5hDxkppc+S3EYz03noDXZkFjkAWNQfxemxMW\nNvLPl5/Mtz/8Dpa3hQF4YschPnvHFr59/3b2DcaJxNN09cfpG07hZGfPh0V1dNARilJTNJUSLRNR\nXNm4kuRIS4SmkJeGgGbdq6mp9hpKy1jPG2P6JnBvxd/3YuBG3I6NtxpjvlXyvOSevxS3Y+OniuuH\niYgNbAH2GGPeN97raUBR02W0Ei3jPTdRxphCDouTNWSc7IjkSICA18p1juwg7PdgW0JjUAOLmrxq\nB5RdQAfQj1scsgl4I/e0McYsm8QN2sBrwEVAN26P+auMMduKzrkUt9Xvpbg95W80xpxR9Pz/xM2R\nadCAomaTbNYwEE+76zTGjNk58rKTF+L32lrORU1atfqh5D0IvN8Y02qMmQO8D/i9MWbpZIJJzhpg\nhzGm0xiTAjYAl5Wccxlwp3FtBJpEZAGAiLQD7wVuneTrKzVjWZbQUuejozlIOODB77X5yOoO7vrs\nGXz8zMUEvFYhOfKTt23mvpf2kcpkOTScpKsvXghEs93D23u4av1Gzv32Q1y1fiMPb++p9S3NaJUG\nlDONMfflHxhj/hM4e4qvvQjoKnrcnTtW6Tn/AvwNkJ3ifSg1Y3lsi7b6AIuag4R8HsJ+D2vPWcpd\nnzmDPz91ER5L6I0m+c7vX2Pt7Zt55LVe0o7DoagGlvzaV89Qgqagl56hBN+4d6sGlSmoNKDsFZGv\ni8iS3J+/A/ZO542NRUTeB/QYY56p4Nx1IrJFRLb09vYegbtT6sjze2zmNwZY2OTuCMt3jrxz7ZpC\n58iu/jj/8JttfC7XOXK2BJbRRiG3PNqJ13arDkiu+oDXFm55tLPGdzxzVRpQrgLmAr/M/WnLHZuK\nPbjrMnntuWOVnHMO8IHc2s4G4AIRuavcixhj1htjVhljVs2dO3eKt6zU1E3nNEvAa7OoKci8hgBe\n22J+Y4CvXrKCW69exdlvmQNQ6Bz55Z++yCv7ImSyWQ5Fk3T3xxlKpKt2L0eDsUYhlbR4VhNTs23D\nIuLBXZS/EDdIbAY+aozZWnTOe4HreHNR/nvGmDUl3+d84K90UV7NBGNtMT5/RdthNcGmsiMMIJJI\nMzCcJpN1Z4a37h3k1sd2jto5EsBrWzTX+QgfA5WNJ1vhYLQqBrNVVfqhiMhvgFEjzlRqeRljMiJy\nHfAA7rbh24wxW0Xk2tzzNwP34QaTHbjbhj892ddT6mhQPM0CEPJ5iKUyhWmW4orG+U/TN8Ckg0q+\nXP5gPM1ALM0JCxv57hUns2V3P7c+tpPXe6I8seMQT+44xJ+dMI+rz1rC/MYAPZEEAx6LljrfiF+4\nM0253jT5UUi+BttoVaHVxI3XArhsDa88reWl1MRMtNNjNT8xO1nDQCxFJFfVuFxypMcSPnDym8mR\nAH6vTXPIOyMDS6VVoquRI3Qsq3otLxGZC2CMmbEr2xpQVK2N9Qsu/2l6MnW/JqK0qnHGyfLA1gPc\n8dTI5MgrTu/g8lXthaZeM7EXy3hTjKoyVQsoIvK/cJMLLdykxgzwr8aYG6pxo0eSBhRVa2P9givt\n9BiJpzkwlMAYOG1xc6HHSrXWV5IZh77hN4tPjpUc+cFTFuHzuHt4Al6b5hkUWHQUMnVVCSi5TPRL\ngHXGmJ25Y8uAm4D7jTH/r0r3e0RoQFFHg9F+wRUHm4yTZc9AAoBFTQGSmSy90RRt9T7m1Pmr+kk7\nnnI4NJwklXEX7qPJTKFzZCLtHpsb9hc6R9q58i1BnxtYAt7JB5Zqb0JQ06NaAeU54CJjzMGS43Nx\nM+VPnfKdHkEaUNTRLv8L9tk3+hFgfmOA+oCXzt4oKSeLz7ZYNtetNFztHUlDiTT9RTvC+oZT3P30\nG/zmhb2FzpHtzUHWFnWOhMkHFp2OmjmqsssL8JYGE3DXUUTEW+4CpVTlyn1Cv2fdmYct3qecLJa4\nf+dVO2eiPuAl7PcwEHNrhOWTIy8/vZ3bn9zFg9sO0N0f54bfbhvROTKecoin4oR8HppC3ooDy1g7\n3qYSUHTUUzvjJTamJvmcUmocYyXdlfZR8dkWWeP+nTdWX5XJJk+KCM11PjpaQjTkAlpxcuQ5hyVH\nvsC2vW5rpFgqw96BOPsHEyQzzlgvAzAtiYVaTqW2xhuhnDxKIy0BAtNwP0rNGmN9Qr/mvGUjciTq\nAx56oykagh6MMWPmTBRPJZXLZyn+BB/22YgIQ8kMHc2hwxb+P3PuEk5qbyKazLC0tY5//OCJbNsb\n4QePdfJC9yDPdw1y3T3PjUiOjKUyxFIZ6vzuiMXvKT9i6SizTbrS5mOTeU911DP9xhyhGGNsY0xD\nmT/1xhid8lJqCsb6hF7aIXJpa5jrLzieJXPCh3WMLDVWjariT/C2wI7eYV7viWIL7DwY5caHdrDr\nULQQiG747Sts2xthYVOwMJW1cmED373C7Rx5fFHnyL+40+0cuX/Q3UwwnMywpz/OgUj5Ecs15y0j\n7RhiKTcvJpbKTDmxUEc9tTXzMpWUOkaM9wn9/BVthwWML1bwfUuzwyPxNAejSXYdirF17yB1fpvG\nYIDO3ii2CAiF/BNLIBLP0BoOHPbpfmFTkOFkhr5c18jVS1o4/bjmEcmRD2w9wB9e6RmRHDmczDCc\nPHzEcv6KNm6Aqm7pnUmjnmORBhSlaqR0WqtapT+Kf6lG4mn2DrpZ8H5biKUc4ikHv8cm5WQLASW/\n2D/ewn+d30PIZxNJZBiIpSAL57+tjXOPbx3ROfIXz+3hvpf3jegcWS6wlAuaUzEd7+lY5VvUSJVW\nG1ZKVVnptNZY01gTUTyVdDCaBEAQ2hoC+D0WCPQOJfHZFsaAyS32V7rwL+K2FO5oDtEU8iEieGyL\n9520gB+vXcM15y2jIeAhkc7y441v8PFbn+Y/tnSRzG0yGG8qbCqm4z0t3SABUx/1HKtqVm24FjQP\nRc0W+UXkTbv68NtuMKkPeAsjlqwxtDcFq5I8mXGyDMTTDOVqhG3q7OPup9+g82CUeNohl8JSNjkS\nGHfxvtY0X2YaankdCzSgqNmmXO2w3qEEsZRDY9BLXW6XVzSZob1ol9dE1zQe3t7DTY/8idf2R4im\nHJqCHppCPoaTDgPxNMl0Fif3u6ajOcinS5IjAcJ+D41HaWCZ7eVbNKCUoQFFzTZH4tN18Wvkc1Dc\nKTY/dT4P8bRD2O9h8ZwQv996oNAPozg5srgg5mRGLLqtd3ppQClDA4qajab703XxKGj7/gi2CAaw\nBdpbQkQTaXqjKVrqfDQFfVgCr+wfKlx/Skcjnz13GdFEhg2bu9gXibOgIcjVZx/HJe9YMG7mvU5J\nTb9qlV6ZViJyMXAjboOtW40x3yp5XnLPX4rbYOtTxphnRaQDuBOYh9sAbL0x5sYjevNK1dBEPpFX\neydVqeJdUD7bIuMYxIJ01pBIORyIJLEtoSHgbrfNZA2fO28ZT+08xPNdbyZH+j0WzSEvDQEPh4aT\n/H8PvEoyneX8FW00hbxs/NOhsj+zbus9etRsl5eI2MD3casZrwSuEpGVJaddAizP/VmHW+UY3BL6\nXzbGrATOBD5f5lqljklHW6Jd8S6o1rCfLAYna/BaQk80iYiwoDGAbVkEvTYeS3iqs49/vtxNjlye\nS45MZrLsjyQ5EEnisQSPJWzY3EUsleFXz+7hb3/1Evsjce0NfxSr5bbhNcAOY0ynMSYFbAAuKznn\nMuBO49oINInIAmPMPmPMswDGmCHgFWDRkbx5pWplrEz4Wijeplwf8DCnzoclQsjvwRh3B1lD0IfX\ntvDablDZH4kjIqxe0sJNHz+NxqD7MwBEEhl2HooxlEizZ8ANChs2d2GL4LUsMllDwGMXfmbd1nv0\nqGVAWQR0FT3u5vCgMO45IrIEOBV4uup3qNRRqNwn8oyT5dk3+idcDLIaypWJueXjp7Pl6xdx2uJm\nPEV5LZYlOMbQ0RLCY7nHLRGWzgkzr97PvHo/ntyW4oF4ht5oivf/6+O8vHcAJ1dWP5s1pJ0stiW8\n0Tc8LSVc1OTM6Ex5EQkDPwe+ZIwpV8QSEVmHO13G4sWLj+DdKTU9SsuLROJp9gwk8Fjli0FOVuk6\nTXHhyNKikvmy+6VGy1z//PnH09ESZDCeZiCW5srVHdz40Ov4PBbHzQmybzBBLOUGkOFcR8l9g0kW\nNELY767XxFMOc8MBls+v5+8uWcHtT+0ed+OB7gabXjXb5SUiZwH/2xjzntzjrwEYY/5P0Tm3AA8b\nY+7JPX4VON8Ysy/Xj+W3wAPGmO9W8pq6y0sdC0p3Ne3ojZJxDO3NQeoD7i/bqTbfKn2Ng9FkIenR\nZ1sjEiI9tjXmrqrxdpk5WUN/LMV/bTvAhk1d7I/EGUpkCHgFY4T+eJriX1P5e3AMXH/BctYsawHG\nb02su8Embybs8toMLBeRpcAe4ErgoyXn3AtcJyIbgDOAwVwwEeCHwCuVBhOljhWlRRXz6xT5YAJT\nX5Qu3Tk1lMgUCkcCI4pKLpsbHnNX1Xi7zGxLaA37+fDp7Vywoo1oMsNVP9hIQ8CDIDSFvPQNpxmI\npwHoGUrh91h8+LR2Vi1tLnyfRNph32B81MCiu8GmX80CijEmIyLXAQ/gbhu+zRizVUSuzT1/M3Af\n7pbhHbjbhj+du/wc4BPASyLyfO7Y3xpj7juSP4NStVL8SzqfB1JsqovSpQURSztGlhaVrMauKq9t\n0dYQoCHtsKgpSO9QMrcrzKKt3k/Qa5HIZBmIpUlmsvxk0xts2d13WHJkPrCUtibWIo/Tr6ZrKLkA\ncF/JsZuLvjbA58tc9zhuky+lZr3pqLBbuk7js61CT3uAjGMKx6G6u6oCXpsvXrCcr//6ZRIZB7/H\nIpHOgghfec8K5jb4ue2JnTyx41Chc+QpHU38xTuX8vYFDYXvk29NnA8sEy1tr+stE6fVhpWa4aaj\nwm7pzqn6gIesgYagh9awD8e4uSatYd+07Ko6f0Ub37zsRBY2BhlOOswJ+wvrJUtb63j/OxayfG64\nsNX4+a4BPv+T5/j7X7/MzoPDI75XPOWwdyDOfz99EclMtqLdYEdbrs9MoaVXlFJllS6mFxeOLC0q\nOZEikpP51J9xsvTH0gwl0mzq7OPGh17HYwl+jzAQzzAQS5PJlTUW4KKV8/jU2UuY3ziyU/mmzj7+\n45kuDkQSLG6pG/X1yxXVnOpGh5lMa3mVoQFFqdqpxi6rZMbhqvUbC+srebFUBo9lkTGG7n63oZjH\nEt5/8kI+nuscWcrnsWgO+ajzHz7zf+63H6Ip6B1RtNIYw2A8zWNfuWCiP/qMV2lA0SkvpdQRUY0M\nf7/HpmcoSdjvGfHLPuizSWQcfvSp1Xz5orfSGvaRyRp++dwePnbr0/zoiZ1Ek5kR3yuVyXIgkqC7\nP3bYc5p9PzkaUJRSR0S1am51NIdIOVm8ttspEoFEOsv8hiC2Jbw31zny2neN3TkyL5XJ0pMLLMO5\nwKLZ95OjAUUpdURU61N//pd9PO1gibu+kjVw5ZqOwjl+r80Vqzq467Nn8PEzFxPwWkQSGW5+pJNP\n3raZ3724Dyc7crq/eMSyemnLtLRnPtbpGopS6oioZqZ6uez7s49vpT+WKowyivUNp7j76Tf4zQt7\nC4v37c1B1pbpHJnn81g0hXyEy6yxzDa6KF+GBhSlautItNJNpB0ORpOkMtnDnts/mOD2J3fx4Lbx\nO0fmeW2L5rrZHVg0oJShAUWp2SOSSNM/nDpsagtg58HhQnJkXr5z5MqFDYedD7M7sGhAKUMDilKz\ni5M1DMRSRBLu4nqpbXsj3Pp4J893DRaOnXP8HNaes5SlrXVlv6fXtmgMeakv2Wl2LNOAUoYGFKVm\np1QmO+r6ijGGLbv7ufWxnbzeEwXAEjc58uqzDk+OzPNYFo1BLw3BYz+waEApQwOKUrNbPOVwaLj8\n+krWGB59rZfbnthVcXIkuNWSG4NeGgJeLOvYDCwaUMrQgKKUgrHXVzJOlvu3HuDOp3ZxMJoCIOC1\nuPz0di5f1THqGoolQkPQS2PQi32MBRYNKGVoQFFK5WVzjb1GW19Jph1+9fxe7tn0BpGEO1XWEPBw\n1ZrFfPCUhfi95Rt5WSLUBzw0Br0j2h/PZBpQytCAopQqlcpk6RtOEUsdvr4CEE1m+OmWLn76TLdb\nRh9oDfv45FlLuOTE+aOORiQXWJqOgcCiAaUMDShKqdEMJzP0DadIO4evr8DkkiPBDSxhv4emkBfv\nDA0sM6I4pIhcLCKvisgOEflqmedFRL6Xe/5FETmt0muVUmoi6vwe2puDtNT5yu7aaqnz8YULjufO\ntWt4zwnzEKC7P84Nv93G5+56ls27+spOnRljGEqk6e6P0zOUGDVgHQtqlqEjIjbwfeAioBvYLCL3\nGmO2FZ12CbA89+cM4CbgjAqvVUqpihX3amlvCnLl6g5O6mg67Lz5jQHe/dY2dh2MsevQMMlMltd7\n3M6RYb+Hdy1vZc9Agn2ROAsagpza0chzXYOFx1eu7uCClW00BX34PKN/pi++n3Cu/8xQMjOhPjJH\nuutkLUcoa4AdxphOY0wK2ABcVnLOZcCdxrURaBKRBRVeq5RSFSnt0NgbTfLd/3qd1w8MHTZNlW/w\nFUtlmFPnHfFLNJrM8LuX97Nt3yB+j0V3/zB3bNzNnoFhGgIeDg0nufGh13loWw/d/TF6IgmSmZEF\nM0vvxxbY0TvM6z1RbKHi7pG16DpZy4CyCOgqetydO1bJOZVcq5RSFRmtV8vtT+6mvTnInDp/YY1k\nw+YuPJZb4LI/lsa2BI8FPlvIT5SlHMMbfXH6Y2kEiCYdBPcajyVs2Oz++oomM+zpj3OgJLAU38/B\naApbBNsSDkZTFfeRqUb/mYmamStEEyAi60Rki4hs6e3trfXtKKWOQmP1ahERGkNeOlpChAMe9kXi\nBLzur860k0UELEvIGoMlUDygcYz7J5HOksm6aycBr8X+SHzEaw3nAsv+wQSJtDPiflK51xBxvy6+\nt8n+TNOllgFlD9BR9Lg9d6yScyq5FgBjzHpjzCpjzKq5c+dO+aaVUseeSnq12JbQVh9gSUsdyVym\nvde2MAaMcb/2eSwECHiEefX+wojFADsPxTgYTRJLOcxvCJa9j1gqw96BOG31foZz25h9Ra/hy0Wr\nSvrI1KLrZC0DymZguYgsFREfcCVwb8k59wKfzO32OhMYNMbsq/BapZSqyEQ6NH7u/LcAQsrJ0hzy\nkjWGbNbQHPJS57PJGqgPeGgIemgJeRBAcANCXyzN3sEEC5sCh3WOLHbF6R0k0lki8RQtdV4cY3Cy\nhtawr+LukbXoOlnTPBQRuRT4F8AGbjPG/JOIXAtgjLlZ3L17/wZcDMSATxtjtox27Xivp3koSqnR\nTKRXS2H3VN8wAa+NMTCcyjC/aFfX/ki88HjL7gE6D0aJpx3y1V5awz6uPmsJF4+SHLmps48Nm7vY\nH4lT57PxWBax3Ahjoru8ptp/RhMby9CAopSaDmknS/9wimiZasbFJpscmef32jQFvdQd4Z4sGlDK\n0ICilJpOibRDfyxFPDX6dBa4nSPveGoXv9/6ZufI5W1hPvvO0TtHFjvSzb40oJShAUUpdSTEUhkO\nRUcv45I3mc6Rxby2RVPIS33AO+V7HosGlDI0oCiljhRjDJF4hv5Yiuw4v2fLdo58yxzWnjt658hi\n091FUgNKGRpQlFJHmpM19A2nGEqkxzyvXOdIwe0c+amzR+8cWWy6AosGlDI0oCilaiWZcTgUTZEY\nY7sw5DtHHuS2J3Ye1jnyY2cspqWufOfIYh7LDSwNgeoEFg0oZWhAUUrVWjSZoS+aKmTOj8bJGu5/\neT93PDWyc+R/P72dK8boHFmsWn3vNaCUoQFFKXU0yGYNA/E0g/F02ZL3xZJph1+/sJefPD2xzpHF\nptr3XgNKGRpQlFJHk7TjdoscHid/BUbvHDlWcmSpyQYWDShlaEBRSh2N4imHQ8NJUpnxm2/1x1Lc\ntXHyyZHgBpaGgJfGYGWBRQNKGRpQlFJHs8F4moFYCic7/u/lqSZHAlgiNATdwDLWCEcDShkaUJRS\nRzsna+iPpYjEx95mnDfV5EgYP7BoQClDA4pSaqZIZhz6hscv45I31eRIABGhIeChMejFU9TYRQNK\nGRpQlFIzzXAyQ9/w+GVc4M3kyB8+vpPXDkwuORLcwFIf8NCUCywaUMrQgKKUmokmUsYlf/6jrx/k\nh49PPjkS3MDSFPTSEvZrQCmlAUUpNZNVWsal+PxyyZGXn97O5RUmRwa8NouaQxUFlJp0bBSRFhF5\nUERez/3dPMp5F4vIqyKyQ0S+WnT8/4rIdhF5UUR+KSJNR+7ulVKqNmxLmFvvZ1FzkKCvsoTG9560\ngB+vXcM15y2jIeAhkc7y441v8PFbn+bfN3eN2TlyomrVAvirwB+MMcuBP+QejyAiNvB94BJgJXCV\niKzMPf0gcKIx5iTgNeBrR+SulVLqKOD32CxoDDK/MYDXHv/XuN9r85HVHdz12TP4xJmLCXgtIokM\ntzzaySdu28RvX9xX0Vbl8dQqoFwG3JH7+g7gg2XOWQPsMMZ0GmNSwIbcdRhjfm+MyaeWbgTap/l+\nlVLqqBPyeWhvDjKnzl9RpnzY7+HT5yzlrs+cwZ+fugiPJRyMpvjug6/x6ds38/CrPRWt0YymVgFl\nnjFmX+7r/cC8MucsArqKHnfnjpVaC/xndW9PKaVmBhGhMeSlvTlEQ9BbUUJjS52PL1xwPHeuXcN7\nTpiHJdDdH+eG377CtXc9y+ZdfePWGCtn2gKKiPyXiLxc5s9lxecZ964nFRJF5O+ADHD3GOesE5Et\nIrKlt7d3Mi+jlFJHPdsSWsN+FjVVtr4CML8xwFcuXsEPPrmKc46fA8COnihf+flLfPmnL7Btb2RC\n9zBtDYmNMf9ttOdE5ICILDDG7BORBUBPmdP2AB1Fj9tzx/Lf41PA+4ALzRih1BizHlgP7i6vCf0Q\nSik1w/g8FgsagxPKX1naWsc/XnbiiOTI57sGue6e53jn8taKX7tWU173Alfnvr4a+HWZczYDy0Vk\nqYj4gCtz1yEiFwN/A3zAGBM7AverlFIzSp3fXV9pDvkq7oWycmED/3z5yXz7w+9geVsYgMdeP1jx\na07bCGUc3wL+Q0Q+A+wGrgAQkYXArcaYS40xGRG5DngAsIHbjDFbc9f/G+AHHsy9URuNMdce6R9C\nKaWOZiJCc52P+oCHvuEU0QrK5IsIq5e0cPpxzTz62kFuf3IXuyt9PU1sVEqp2SGRdjgYraxMfp7H\nFo6bEz56ExuVUkodeQGvTXtziDnhyrYZg9tGuFIaUJRSapZpDL65zbiaNKAopdQsVNhm3BwkUEFf\n+kpoQFFKqVnM77FZ2BSkraGyMi5jqdUuL6WUUkeRsN9Dnc/OtSFOT6oEi45QlFJKAbn+JyEf7c1B\nwmXj7C4AAAVySURBVIGJjzd0hKKUUmoEj23RVh+gIeAQq7AFMWhAUUopNYqA157Qgr1OeSmllKoK\nDShKKaWqQgOKUkqpqtCAopRSqio0oCillKoKDShKKaWqQgOKUkqpqtCAopRSqio0oCillKqKWdWx\nUUR6oeJulgCtQOUNlWcXfW/K0/dldPrejO5of2+OM8bMHe+kWRVQJkpEtlTS9nI20vemPH1fRqfv\nzeiOlfdGp7yUUkpVhQYUpZRSVaEBZWzra30DRzF9b8rT92V0+t6M7ph4b3QNRSmlVFXoCEUppVRV\naEABRKRDRP4oIttEZKuIXJ873iIiD4rI67m/m2t9r7UiIraIPCciv8091vcGEJEmEfmZiGwXkVdE\n5Cx9b0BE/jL3/9LLInKPiARm6/siIreJSI+IvFx0bNT3QkS+JiI7RORVEXlPbe56cjSguDLAl40x\nK4Ezgc+LyErgq8AfjDHL///27izUqiqO4/j3V1Y4kFSEmAP6UNkAZUhYRkkKBUkWQflgSAPRQ5kP\nEVQP1Uv0EFEQ9pANQoNUDvmSCUmT4IDNaESkOHQdwDIrGqBfD3tJm0uH1DZnZ/v3gQtnr70OLH5w\nz//svfZZC3inHHfVPcCW2nGyqTwFrLI9CbiAKqNOZyNpDDAfmGL7fOB4YA7dzeVF4OpBbX+bRfnc\nmQOcV96zUNLhb5nYshQUwPaA7Y/K64NUHwpjgNnA4tJtMXBdOyNsl6SxwDXAolpz57ORNBK4HHgO\nwPZvtr8n2UC1vfhQSUOAYcC3dDQX2+8D+wc198piNrDE9q+2twJfAxf3ZaANSEEZRNIEYDKwHhhl\ne6Cc2g2MamlYbXsSuA/4o9aWbGAisA94odwOXCRpOB3PxvYu4HFgOzAAHLC9mo7nMkivLMYAO2r9\ndpa2Y0IKSo2kEcBSYIHtH+rnXD0O17lH4iTNAvba3tSrT1ezofoWfhHwjO3JwE8Muo3TxWzKfMBs\nqoJ7BjBc0tx6ny7m0sv/KYsUlELSCVTF5GXby0rzHkmjy/nRwN62xteiacC1krYBS4ArJb1EsoHq\n2+NO2+vL8RtUBabr2cwEttreZ/t3YBlwKcmlrlcWu4BxtX5jS9sxIQUFkCSq++BbbD9RO7USmFde\nzwPe7PfY2mb7fttjbU+gmixcY3suyQbbu4Edks4uTTOAzSSb7cBUScPK/9YMqnnJrudS1yuLlcAc\nSSdJmgicCWxoYXxHJT9sBCRdBnwAfM5f8wQPUM2jvAaMp1ql+EbbgyfXOkPSdOBe27MknUayQdKF\nVA8rnAh8A9xC9UWt09lIegS4ieoJyo+B24ERdDAXSa8C06lWFN4DPASsoEcWkh4EbqXKboHtt1oY\n9lFJQYmIiEbklldERDQiBSUiIhqRghIREY1IQYmIiEakoERERCNSUCIiohEpKBER0YgUlIg+kbRC\n0qayT8gdpe02SV9J2iDpWUlPl/bTJS2VtLH8TWt39BH/LD9sjOgTSafa3i9pKLARuApYS7X+10Fg\nDfCp7bskvQIstP2hpPHA27bPaW3wEYdhSNsDiOiQ+ZKuL6/HATcD79WW3HgdOKucnwmcWy2FBcDJ\nkkbY/rGfA444EikoEX1Q1kGbCVxi+2dJ7wJfAr2uOo4Dptr+pT8jjPj3MocS0R8jge9KMZlEtdX0\ncOAKSaeUnQ1vqPVfDdx96KAsQhnxn5aCEtEfq4AhkrYAjwHrqPa5eJRqefK1wDbgQOk/H5gi6TNJ\nm4E7+z7iiCOUSfmIFh2aFylXKMuB520vb3tcEUcjVygR7XpY0ifAF8BWqn0yIo5JuUKJiIhG5Aol\nIiIakYISERGNSEGJiIhGpKBEREQjUlAiIqIRKSgREdGIPwFRcjhORdl6/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6ea0c5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## remove outlier\n",
    "train_df = train_df[train_df['age'] != 0]\n",
    "train_df = train_df[train_df['age'] !=99]\n",
    "train_df = train_df[train_df['age'] !=101]\n",
    "grouped_df = train_df.groupby('age')\n",
    "dlinq_age = grouped_df['SeriousDlqin2yrs'].aggregate([np.mean,'count']).reset_index()\n",
    "dlinq_age.columns =['age','DlqinFreq','count']\n",
    "sns.regplot(x='age',y='DlqinFreq',data=dlinq_age)\n",
    "plt.show()\n",
    "\n",
    "## create new features\n",
    "train_df['age_sqr'] = train_df['age'].values^2 \n",
    "## apply the same operation on testing set\n",
    "test_df['age_sqr'] = test_df['age'].values^2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split training data into 5 fold. Run xgboost and plot feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "(146067, 18)\n",
      "Index(['RevolvingUtilizationOfUnsecuredLines', 'age',\n",
      "       'NumberOfTime30-59DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome',\n",
      "       'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate',\n",
      "       'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
      "       'NumberOfDependents', 'MonthlyIncome_Null', 'NoD_Null',\n",
      "       'IncomePerPerson', 'NumOfPastDue', 'MonthlyDebt',\n",
      "       'NumOfOpenCreditLines', 'MonthlyBalance', 'age_sqr'],\n",
      "      dtype='object')\n",
      "(101503, 16)\n"
     ]
    }
   ],
   "source": [
    "train_y = train_df['SeriousDlqin2yrs']\n",
    "#'RevolvingUtilizationOfUnsecuredLines'\n",
    "train_X = train_df.drop(['SeriousDlqin2yrs','ID'],axis=1,inplace=False)\n",
    "test_X = test_df.drop(['SeriousDlqin2yrs','ID'],axis=1,inplace=False)\n",
    "print(type(train_y))\n",
    "skf = model_selection.StratifiedKFold(n_splits=5,random_state=100)\n",
    "xgb_params = {\n",
    "'eta':0.03,\n",
    "'max_depth':4,\n",
    "'sub_sample':0.9,\n",
    "'colsample_bytree':0.5,\n",
    "'objective':'binary:logistic',\n",
    "'eval_metric':'auc',\n",
    "'silent':0\n",
    "}\n",
    "\n",
    "print(train_X.shape)\n",
    "print(train_X.columns)\n",
    "print(test_X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.812817\tval-auc:0.810043\n",
      "Multiple eval metrics have been passed: 'val-auc' will be used for early stopping.\n",
      "\n",
      "Will train until val-auc hasn't improved in 30 rounds.\n",
      "[1]\ttrain-auc:0.823345\tval-auc:0.821208\n",
      "[2]\ttrain-auc:0.850152\tval-auc:0.845294\n",
      "[3]\ttrain-auc:0.850522\tval-auc:0.847108\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[196]\ttrain-auc:0.871041\tval-auc:0.861124\n",
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      "[273]\ttrain-auc:0.874365\tval-auc:0.86194\n",
      "[274]\ttrain-auc:0.874409\tval-auc:0.861926\n",
      "[275]\ttrain-auc:0.874439\tval-auc:0.861928\n",
      "[276]\ttrain-auc:0.874485\tval-auc:0.861943\n",
      "[277]\ttrain-auc:0.87452\tval-auc:0.861949\n",
      "[278]\ttrain-auc:0.874549\tval-auc:0.861931\n",
      "[279]\ttrain-auc:0.874584\tval-auc:0.861937\n",
      "[280]\ttrain-auc:0.874615\tval-auc:0.861957\n",
      "[281]\ttrain-auc:0.874646\tval-auc:0.861946\n",
      "[282]\ttrain-auc:0.874674\tval-auc:0.86193\n",
      "[283]\ttrain-auc:0.874685\tval-auc:0.861932\n",
      "[284]\ttrain-auc:0.87473\tval-auc:0.861927\n",
      "[285]\ttrain-auc:0.874739\tval-auc:0.861927\n",
      "[286]\ttrain-auc:0.874756\tval-auc:0.861943\n",
      "[287]\ttrain-auc:0.874792\tval-auc:0.861938\n",
      "[288]\ttrain-auc:0.8748\tval-auc:0.861938\n",
      "[289]\ttrain-auc:0.874846\tval-auc:0.861935\n",
      "[290]\ttrain-auc:0.874867\tval-auc:0.86196\n",
      "[291]\ttrain-auc:0.874911\tval-auc:0.861941\n",
      "[292]\ttrain-auc:0.87494\tval-auc:0.861948\n",
      "[293]\ttrain-auc:0.874965\tval-auc:0.861945\n",
      "[294]\ttrain-auc:0.875\tval-auc:0.861957\n",
      "[295]\ttrain-auc:0.875029\tval-auc:0.861949\n",
      "[296]\ttrain-auc:0.87506\tval-auc:0.861961\n",
      "[297]\ttrain-auc:0.875089\tval-auc:0.861985\n",
      "[298]\ttrain-auc:0.875097\tval-auc:0.861985\n",
      "[299]\ttrain-auc:0.875124\tval-auc:0.861983\n",
      "[300]\ttrain-auc:0.875166\tval-auc:0.861987\n",
      "[301]\ttrain-auc:0.875184\tval-auc:0.861984\n",
      "[302]\ttrain-auc:0.8752\tval-auc:0.861985\n",
      "[303]\ttrain-auc:0.875207\tval-auc:0.861986\n",
      "[304]\ttrain-auc:0.875214\tval-auc:0.861986\n",
      "[305]\ttrain-auc:0.875256\tval-auc:0.861972\n",
      "[306]\ttrain-auc:0.875289\tval-auc:0.862003\n",
      "[307]\ttrain-auc:0.875298\tval-auc:0.862009\n",
      "[308]\ttrain-auc:0.875312\tval-auc:0.862012\n",
      "[309]\ttrain-auc:0.875346\tval-auc:0.861994\n",
      "[310]\ttrain-auc:0.875367\tval-auc:0.861984\n",
      "[311]\ttrain-auc:0.875387\tval-auc:0.861984\n",
      "[312]\ttrain-auc:0.875404\tval-auc:0.861989\n",
      "[313]\ttrain-auc:0.875434\tval-auc:0.861976\n",
      "[314]\ttrain-auc:0.875473\tval-auc:0.861978\n",
      "[315]\ttrain-auc:0.875495\tval-auc:0.861969\n",
      "[316]\ttrain-auc:0.875513\tval-auc:0.861978\n",
      "[317]\ttrain-auc:0.875536\tval-auc:0.861981\n",
      "[318]\ttrain-auc:0.875567\tval-auc:0.861968\n",
      "[319]\ttrain-auc:0.875599\tval-auc:0.861968\n",
      "[320]\ttrain-auc:0.875647\tval-auc:0.861983\n",
      "[321]\ttrain-auc:0.875657\tval-auc:0.861989\n",
      "[322]\ttrain-auc:0.875671\tval-auc:0.861975\n",
      "[323]\ttrain-auc:0.875714\tval-auc:0.861979\n",
      "[324]\ttrain-auc:0.875723\tval-auc:0.861985\n",
      "[325]\ttrain-auc:0.875732\tval-auc:0.861993\n",
      "[326]\ttrain-auc:0.875745\tval-auc:0.861987\n",
      "[327]\ttrain-auc:0.875759\tval-auc:0.861982\n",
      "[328]\ttrain-auc:0.875782\tval-auc:0.861979\n",
      "[329]\ttrain-auc:0.875788\tval-auc:0.862\n",
      "[330]\ttrain-auc:0.875829\tval-auc:0.861996\n",
      "[331]\ttrain-auc:0.875862\tval-auc:0.862005\n",
      "[332]\ttrain-auc:0.875911\tval-auc:0.862007\n",
      "[333]\ttrain-auc:0.875955\tval-auc:0.862023\n",
      "[334]\ttrain-auc:0.875988\tval-auc:0.862017\n",
      "[335]\ttrain-auc:0.876017\tval-auc:0.862004\n",
      "[336]\ttrain-auc:0.876025\tval-auc:0.862004\n",
      "[337]\ttrain-auc:0.876034\tval-auc:0.862009\n",
      "[338]\ttrain-auc:0.876063\tval-auc:0.861998\n",
      "[339]\ttrain-auc:0.876084\tval-auc:0.861997\n",
      "[340]\ttrain-auc:0.87609\tval-auc:0.861998\n",
      "[341]\ttrain-auc:0.876101\tval-auc:0.861995\n",
      "[342]\ttrain-auc:0.876114\tval-auc:0.861993\n",
      "[343]\ttrain-auc:0.876119\tval-auc:0.861992\n",
      "[344]\ttrain-auc:0.876156\tval-auc:0.861988\n",
      "[345]\ttrain-auc:0.876182\tval-auc:0.861986\n",
      "[346]\ttrain-auc:0.876203\tval-auc:0.861972\n",
      "[347]\ttrain-auc:0.876235\tval-auc:0.861976\n",
      "[348]\ttrain-auc:0.87627\tval-auc:0.861972\n",
      "[349]\ttrain-auc:0.876291\tval-auc:0.861973\n",
      "[350]\ttrain-auc:0.876345\tval-auc:0.861977\n",
      "[351]\ttrain-auc:0.876352\tval-auc:0.861977\n",
      "[352]\ttrain-auc:0.876383\tval-auc:0.861977\n",
      "[353]\ttrain-auc:0.876412\tval-auc:0.861996\n",
      "[354]\ttrain-auc:0.876418\tval-auc:0.861996\n",
      "[355]\ttrain-auc:0.876465\tval-auc:0.861977\n",
      "[356]\ttrain-auc:0.876484\tval-auc:0.861987\n",
      "[357]\ttrain-auc:0.876505\tval-auc:0.861983\n",
      "[358]\ttrain-auc:0.87651\tval-auc:0.861982\n",
      "[359]\ttrain-auc:0.876541\tval-auc:0.861996\n",
      "[360]\ttrain-auc:0.876569\tval-auc:0.862\n",
      "[361]\ttrain-auc:0.876597\tval-auc:0.862008\n",
      "[362]\ttrain-auc:0.876655\tval-auc:0.861992\n",
      "[363]\ttrain-auc:0.87666\tval-auc:0.861991\n",
      "Stopping. Best iteration:\n",
      "[333]\ttrain-auc:0.875955\tval-auc:0.862023\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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IVCkz+0jSFOAEM3s0/pJ+P94qWUhIUOaZ2XRJWwDfmFnq9snzhMnWkwm3ly41s//FCd9J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\nVFLKNt26JQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x284570ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.818206\tval-auc:0.81444\n",
      "Multiple eval metrics have been passed: 'val-auc' will be used for early stopping.\n",
      "\n",
      "Will train until val-auc hasn't improved in 30 rounds.\n",
      "[1]\ttrain-auc:0.821651\tval-auc:0.819504\n",
      "[2]\ttrain-auc:0.845786\tval-auc:0.842739\n",
      "[3]\ttrain-auc:0.843884\tval-auc:0.840385\n",
      "[4]\ttrain-auc:0.85078\tval-auc:0.847521\n",
      "[5]\ttrain-auc:0.852256\tval-auc:0.847944\n",
      "[6]\ttrain-auc:0.851157\tval-auc:0.846979\n",
      "[7]\ttrain-auc:0.855012\tval-auc:0.850635\n",
      "[8]\ttrain-auc:0.858932\tval-auc:0.854222\n",
      "[9]\ttrain-auc:0.859242\tval-auc:0.854155\n",
      "[10]\ttrain-auc:0.858337\tval-auc:0.853393\n",
      "[11]\ttrain-auc:0.857874\tval-auc:0.852793\n",
      "[12]\ttrain-auc:0.858239\tval-auc:0.853041\n",
      "[13]\ttrain-auc:0.858674\tval-auc:0.853548\n",
      "[14]\ttrain-auc:0.859541\tval-auc:0.854668\n",
      "[15]\ttrain-auc:0.86018\tval-auc:0.855593\n",
      "[16]\ttrain-auc:0.860223\tval-auc:0.855275\n",
      "[17]\ttrain-auc:0.860119\tval-auc:0.8551\n",
      "[18]\ttrain-auc:0.859953\tval-auc:0.8548\n",
      "[19]\ttrain-auc:0.859674\tval-auc:0.854351\n",
      "[20]\ttrain-auc:0.859372\tval-auc:0.854091\n",
      "[21]\ttrain-auc:0.859524\tval-auc:0.854239\n",
      "[22]\ttrain-auc:0.860435\tval-auc:0.85505\n",
      "[23]\ttrain-auc:0.86041\tval-auc:0.854989\n",
      "[24]\ttrain-auc:0.860368\tval-auc:0.854771\n",
      "[25]\ttrain-auc:0.860724\tval-auc:0.855266\n",
      "[26]\ttrain-auc:0.860517\tval-auc:0.854968\n",
      "[27]\ttrain-auc:0.860974\tval-auc:0.855538\n",
      "[28]\ttrain-auc:0.860735\tval-auc:0.855272\n",
      "[29]\ttrain-auc:0.860823\tval-auc:0.85554\n",
      "[30]\ttrain-auc:0.860664\tval-auc:0.855314\n",
      "[31]\ttrain-auc:0.860767\tval-auc:0.855353\n",
      "[32]\ttrain-auc:0.860493\tval-auc:0.855129\n",
      "[33]\ttrain-auc:0.860234\tval-auc:0.854902\n",
      "[34]\ttrain-auc:0.859922\tval-auc:0.854463\n",
      "[35]\ttrain-auc:0.86001\tval-auc:0.854508\n",
      "[36]\ttrain-auc:0.859828\tval-auc:0.854385\n",
      "[37]\ttrain-auc:0.860084\tval-auc:0.854622\n",
      "[38]\ttrain-auc:0.85996\tval-auc:0.854562\n",
      "[39]\ttrain-auc:0.860685\tval-auc:0.855192\n",
      "[40]\ttrain-auc:0.860454\tval-auc:0.855005\n",
      "[41]\ttrain-auc:0.860631\tval-auc:0.855111\n",
      "[42]\ttrain-auc:0.860492\tval-auc:0.855\n",
      "[43]\ttrain-auc:0.861164\tval-auc:0.855591\n",
      "[44]\ttrain-auc:0.861111\tval-auc:0.855327\n",
      "[45]\ttrain-auc:0.861026\tval-auc:0.855201\n",
      "Stopping. Best iteration:\n",
      "[15]\ttrain-auc:0.86018\tval-auc:0.855593\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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eey7Tpk1j3333ZeDAga5952yybOw1UvcBZ0uqU4prWgMXEx48fwX2MLN2hDo7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\nkXLOOeecK9D/A27gR6xtyRmYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28456c3400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.812624\tval-auc:0.810845\n",
      "Multiple eval metrics have been passed: 'val-auc' will be used for early stopping.\n",
      "\n",
      "Will train until val-auc hasn't improved in 30 rounds.\n",
      "[1]\ttrain-auc:0.817235\tval-auc:0.812497\n",
      "[2]\ttrain-auc:0.846773\tval-auc:0.84438\n",
      "[3]\ttrain-auc:0.846466\tval-auc:0.84502\n",
      "[4]\ttrain-auc:0.849255\tval-auc:0.847186\n",
      "[5]\ttrain-auc:0.852137\tval-auc:0.84943\n",
      "[6]\ttrain-auc:0.851838\tval-auc:0.8493\n",
      "[7]\ttrain-auc:0.854518\tval-auc:0.851847\n",
      "[8]\ttrain-auc:0.858225\tval-auc:0.855089\n",
      "[9]\ttrain-auc:0.858369\tval-auc:0.855051\n",
      "[10]\ttrain-auc:0.857301\tval-auc:0.853749\n",
      "[11]\ttrain-auc:0.856926\tval-auc:0.853682\n",
      "[12]\ttrain-auc:0.857198\tval-auc:0.853709\n",
      "[13]\ttrain-auc:0.857555\tval-auc:0.854498\n",
      "[14]\ttrain-auc:0.858845\tval-auc:0.855782\n",
      "[15]\ttrain-auc:0.859591\tval-auc:0.856303\n",
      "[16]\ttrain-auc:0.859674\tval-auc:0.856462\n",
      "[17]\ttrain-auc:0.859466\tval-auc:0.856129\n",
      "[18]\ttrain-auc:0.85927\tval-auc:0.85606\n",
      "[19]\ttrain-auc:0.859041\tval-auc:0.856115\n",
      "[20]\ttrain-auc:0.85866\tval-auc:0.855494\n",
      "[21]\ttrain-auc:0.858795\tval-auc:0.855487\n",
      "[22]\ttrain-auc:0.85988\tval-auc:0.856378\n",
      "[23]\ttrain-auc:0.859732\tval-auc:0.856288\n",
      "[24]\ttrain-auc:0.859472\tval-auc:0.856035\n",
      "[25]\ttrain-auc:0.860057\tval-auc:0.856589\n",
      "[26]\ttrain-auc:0.859815\tval-auc:0.856412\n",
      "[27]\ttrain-auc:0.860374\tval-auc:0.857024\n",
      "[28]\ttrain-auc:0.860117\tval-auc:0.856727\n",
      "[29]\ttrain-auc:0.860389\tval-auc:0.85689\n",
      "[30]\ttrain-auc:0.860272\tval-auc:0.856713\n",
      "[31]\ttrain-auc:0.860344\tval-auc:0.856861\n",
      "[32]\ttrain-auc:0.860304\tval-auc:0.856773\n",
      "[33]\ttrain-auc:0.860051\tval-auc:0.856406\n",
      "[34]\ttrain-auc:0.859817\tval-auc:0.856141\n",
      "[35]\ttrain-auc:0.860001\tval-auc:0.856574\n",
      "[36]\ttrain-auc:0.859773\tval-auc:0.856443\n",
      "[37]\ttrain-auc:0.859857\tval-auc:0.856587\n",
      "[38]\ttrain-auc:0.859525\tval-auc:0.856178\n",
      "[39]\ttrain-auc:0.860222\tval-auc:0.856888\n",
      "[40]\ttrain-auc:0.860039\tval-auc:0.85673\n",
      "[41]\ttrain-auc:0.860239\tval-auc:0.857002\n",
      "[42]\ttrain-auc:0.860067\tval-auc:0.856761\n",
      "[43]\ttrain-auc:0.860737\tval-auc:0.857367\n",
      "[44]\ttrain-auc:0.860515\tval-auc:0.857062\n",
      "[45]\ttrain-auc:0.860528\tval-auc:0.856937\n",
      "[46]\ttrain-auc:0.860757\tval-auc:0.856891\n",
      "[47]\ttrain-auc:0.861068\tval-auc:0.857255\n",
      "[48]\ttrain-auc:0.860826\tval-auc:0.857026\n",
      "[49]\ttrain-auc:0.860698\tval-auc:0.856876\n",
      "[50]\ttrain-auc:0.860891\tval-auc:0.857144\n",
      "[51]\ttrain-auc:0.861382\tval-auc:0.857743\n",
      "[52]\ttrain-auc:0.861485\tval-auc:0.857683\n",
      "[53]\ttrain-auc:0.861516\tval-auc:0.857801\n",
      "[54]\ttrain-auc:0.861404\tval-auc:0.857583\n",
      "[55]\ttrain-auc:0.861852\tval-auc:0.858167\n",
      "[56]\ttrain-auc:0.862055\tval-auc:0.858429\n",
      "[57]\ttrain-auc:0.861967\tval-auc:0.858272\n",
      "[58]\ttrain-auc:0.861943\tval-auc:0.858117\n",
      "[59]\ttrain-auc:0.862115\tval-auc:0.858284\n",
      "[60]\ttrain-auc:0.86229\tval-auc:0.858496\n",
      "[61]\ttrain-auc:0.862501\tval-auc:0.858772\n",
      "[62]\ttrain-auc:0.862598\tval-auc:0.858857\n",
      "[63]\ttrain-auc:0.862641\tval-auc:0.858995\n",
      "[64]\ttrain-auc:0.862574\tval-auc:0.858852\n",
      "[65]\ttrain-auc:0.862552\tval-auc:0.858862\n",
      "[66]\ttrain-auc:0.862621\tval-auc:0.858924\n",
      "[67]\ttrain-auc:0.862715\tval-auc:0.859033\n",
      "[68]\ttrain-auc:0.86277\tval-auc:0.859128\n",
      "[69]\ttrain-auc:0.862762\tval-auc:0.859088\n",
      "[70]\ttrain-auc:0.862807\tval-auc:0.859082\n",
      "[71]\ttrain-auc:0.862833\tval-auc:0.859072\n",
      "[72]\ttrain-auc:0.862984\tval-auc:0.859174\n",
      "[73]\ttrain-auc:0.863006\tval-auc:0.859095\n",
      "[74]\ttrain-auc:0.863122\tval-auc:0.859206\n",
      "[75]\ttrain-auc:0.8632\tval-auc:0.859366\n",
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      "[353]\ttrain-auc:0.876062\tval-auc:0.864454\n",
      "[354]\ttrain-auc:0.87607\tval-auc:0.864457\n",
      "[355]\ttrain-auc:0.876077\tval-auc:0.864458\n",
      "[356]\ttrain-auc:0.876084\tval-auc:0.86446\n",
      "[357]\ttrain-auc:0.876097\tval-auc:0.864461\n",
      "[358]\ttrain-auc:0.876138\tval-auc:0.864493\n",
      "[359]\ttrain-auc:0.876146\tval-auc:0.864495\n",
      "[360]\ttrain-auc:0.876171\tval-auc:0.864507\n",
      "[361]\ttrain-auc:0.876226\tval-auc:0.864512\n",
      "[362]\ttrain-auc:0.876254\tval-auc:0.864499\n",
      "[363]\ttrain-auc:0.876285\tval-auc:0.864495\n",
      "[364]\ttrain-auc:0.876324\tval-auc:0.864499\n",
      "[365]\ttrain-auc:0.876355\tval-auc:0.864495\n",
      "[366]\ttrain-auc:0.876364\tval-auc:0.864493\n",
      "[367]\ttrain-auc:0.876383\tval-auc:0.864501\n",
      "[368]\ttrain-auc:0.876392\tval-auc:0.864501\n",
      "[369]\ttrain-auc:0.876416\tval-auc:0.864489\n",
      "[370]\ttrain-auc:0.876452\tval-auc:0.864456\n",
      "[371]\ttrain-auc:0.876485\tval-auc:0.864459\n",
      "[372]\ttrain-auc:0.876496\tval-auc:0.864457\n",
      "[373]\ttrain-auc:0.876541\tval-auc:0.864449\n",
      "[374]\ttrain-auc:0.876555\tval-auc:0.864457\n",
      "[375]\ttrain-auc:0.876574\tval-auc:0.864451\n",
      "[376]\ttrain-auc:0.876603\tval-auc:0.864437\n",
      "[377]\ttrain-auc:0.876615\tval-auc:0.864436\n",
      "[378]\ttrain-auc:0.876623\tval-auc:0.864436\n",
      "[379]\ttrain-auc:0.876631\tval-auc:0.864433\n",
      "[380]\ttrain-auc:0.876654\tval-auc:0.86442\n",
      "[381]\ttrain-auc:0.876672\tval-auc:0.864419\n",
      "[382]\ttrain-auc:0.876684\tval-auc:0.864416\n",
      "[383]\ttrain-auc:0.876721\tval-auc:0.864415\n",
      "[384]\ttrain-auc:0.876772\tval-auc:0.864422\n",
      "[385]\ttrain-auc:0.876803\tval-auc:0.864417\n",
      "[386]\ttrain-auc:0.876819\tval-auc:0.8644\n",
      "[387]\ttrain-auc:0.876838\tval-auc:0.864412\n",
      "[388]\ttrain-auc:0.876848\tval-auc:0.86441\n",
      "[389]\ttrain-auc:0.876883\tval-auc:0.864417\n",
      "[390]\ttrain-auc:0.876934\tval-auc:0.864423\n",
      "[391]\ttrain-auc:0.876958\tval-auc:0.864426\n",
      "Stopping. Best iteration:\n",
      "[361]\ttrain-auc:0.876226\tval-auc:0.864512\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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R8gMwV9Ix8X5J6lwJTY6TCMoyK3acjRGfkXISjSXXwLhczOwnSccBd0pqRFiS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\nu8ryDwSeAGaaWZ0ccN1rr3C23357XnjhBcrKypgwYQK77bY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2YMjMRgAjEvsdcx3LZ0ac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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x284570d5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.814591\tval-auc:0.805664\n",
      "Multiple eval metrics have been passed: 'val-auc' will be used for early stopping.\n",
      "\n",
      "Will train until val-auc hasn't improved in 30 rounds.\n",
      "[1]\ttrain-auc:0.815614\tval-auc:0.805984\n",
      "[2]\ttrain-auc:0.84272\tval-auc:0.835372\n",
      "[3]\ttrain-auc:0.844697\tval-auc:0.836772\n",
      "[4]\ttrain-auc:0.848391\tval-auc:0.842718\n",
      "[5]\ttrain-auc:0.8519\tval-auc:0.847051\n",
      "[6]\ttrain-auc:0.851014\tval-auc:0.845613\n",
      "[7]\ttrain-auc:0.854534\tval-auc:0.850037\n",
      "[8]\ttrain-auc:0.858393\tval-auc:0.854725\n",
      "[9]\ttrain-auc:0.858631\tval-auc:0.85501\n",
      "[10]\ttrain-auc:0.857836\tval-auc:0.853994\n",
      "[11]\ttrain-auc:0.857408\tval-auc:0.853544\n",
      "[12]\ttrain-auc:0.858255\tval-auc:0.854418\n",
      "[13]\ttrain-auc:0.858305\tval-auc:0.854651\n",
      "[14]\ttrain-auc:0.85866\tval-auc:0.85495\n",
      "[15]\ttrain-auc:0.859351\tval-auc:0.856026\n",
      "[16]\ttrain-auc:0.859431\tval-auc:0.856142\n",
      "[17]\ttrain-auc:0.859425\tval-auc:0.855798\n",
      "[18]\ttrain-auc:0.859322\tval-auc:0.855717\n",
      "[19]\ttrain-auc:0.85931\tval-auc:0.855762\n",
      "[20]\ttrain-auc:0.859083\tval-auc:0.855508\n",
      "[21]\ttrain-auc:0.859243\tval-auc:0.855619\n",
      "[22]\ttrain-auc:0.860063\tval-auc:0.856606\n",
      "[23]\ttrain-auc:0.86005\tval-auc:0.856425\n",
      "[24]\ttrain-auc:0.859822\tval-auc:0.856138\n",
      "[25]\ttrain-auc:0.860216\tval-auc:0.856702\n",
      "[26]\ttrain-auc:0.860037\tval-auc:0.856429\n",
      "[27]\ttrain-auc:0.8605\tval-auc:0.856977\n",
      "[28]\ttrain-auc:0.860313\tval-auc:0.856744\n",
      "[29]\ttrain-auc:0.860651\tval-auc:0.857015\n",
      "[30]\ttrain-auc:0.860458\tval-auc:0.856862\n",
      "[31]\ttrain-auc:0.860546\tval-auc:0.857042\n",
      "[32]\ttrain-auc:0.860368\tval-auc:0.856612\n",
      "[33]\ttrain-auc:0.860176\tval-auc:0.85632\n",
      "[34]\ttrain-auc:0.85987\tval-auc:0.85603\n",
      "[35]\ttrain-auc:0.860023\tval-auc:0.856165\n",
      "[36]\ttrain-auc:0.859769\tval-auc:0.855996\n",
      "[37]\ttrain-auc:0.859858\tval-auc:0.856171\n",
      "[38]\ttrain-auc:0.859621\tval-auc:0.855919\n",
      "[39]\ttrain-auc:0.860403\tval-auc:0.856665\n",
      "[40]\ttrain-auc:0.860144\tval-auc:0.85635\n",
      "[41]\ttrain-auc:0.860408\tval-auc:0.856613\n",
      "[42]\ttrain-auc:0.860271\tval-auc:0.856416\n",
      "[43]\ttrain-auc:0.86103\tval-auc:0.857068\n",
      "[44]\ttrain-auc:0.860866\tval-auc:0.856974\n",
      "[45]\ttrain-auc:0.860726\tval-auc:0.856789\n",
      "[46]\ttrain-auc:0.860958\tval-auc:0.856771\n",
      "[47]\ttrain-auc:0.861371\tval-auc:0.857237\n",
      "[48]\ttrain-auc:0.861107\tval-auc:0.856971\n",
      "[49]\ttrain-auc:0.860967\tval-auc:0.856703\n",
      "[50]\ttrain-auc:0.861133\tval-auc:0.856864\n",
      "[51]\ttrain-auc:0.86161\tval-auc:0.857372\n",
      "[52]\ttrain-auc:0.861734\tval-auc:0.857311\n",
      "[53]\ttrain-auc:0.861838\tval-auc:0.857483\n",
      "[54]\ttrain-auc:0.861679\tval-auc:0.85724\n",
      "[55]\ttrain-auc:0.862094\tval-auc:0.857766\n",
      "[56]\ttrain-auc:0.862226\tval-auc:0.857909\n",
      "[57]\ttrain-auc:0.862131\tval-auc:0.857782\n",
      "[58]\ttrain-auc:0.86211\tval-auc:0.857672\n",
      "[59]\ttrain-auc:0.862248\tval-auc:0.857836\n",
      "[60]\ttrain-auc:0.862389\tval-auc:0.857992\n",
      "[61]\ttrain-auc:0.862602\tval-auc:0.858363\n",
      "[62]\ttrain-auc:0.86272\tval-auc:0.8584\n",
      "[63]\ttrain-auc:0.862789\tval-auc:0.858516\n",
      "[64]\ttrain-auc:0.862771\tval-auc:0.858436\n",
      "[65]\ttrain-auc:0.862799\tval-auc:0.858512\n",
      "[66]\ttrain-auc:0.862854\tval-auc:0.858527\n",
      "[67]\ttrain-auc:0.862941\tval-auc:0.858544\n",
      "[68]\ttrain-auc:0.863053\tval-auc:0.858657\n",
      "[69]\ttrain-auc:0.863052\tval-auc:0.858673\n",
      "[70]\ttrain-auc:0.863055\tval-auc:0.858497\n",
      "[71]\ttrain-auc:0.863057\tval-auc:0.858529\n",
      "[72]\ttrain-auc:0.863282\tval-auc:0.858744\n",
      "[73]\ttrain-auc:0.863294\tval-auc:0.85865\n",
      "[74]\ttrain-auc:0.863447\tval-auc:0.858759\n",
      "[75]\ttrain-auc:0.863596\tval-auc:0.858938\n",
      "[76]\ttrain-auc:0.863533\tval-auc:0.858816\n",
      "[77]\ttrain-auc:0.863738\tval-auc:0.858818\n",
      "[78]\ttrain-auc:0.863801\tval-auc:0.858891\n",
      "[79]\ttrain-auc:0.863776\tval-auc:0.858827\n",
      "[80]\ttrain-auc:0.86378\tval-auc:0.858754\n",
      "[81]\ttrain-auc:0.863715\tval-auc:0.858674\n",
      "[82]\ttrain-auc:0.86387\tval-auc:0.858809\n",
      "[83]\ttrain-auc:0.863863\tval-auc:0.858685\n",
      "[84]\ttrain-auc:0.863819\tval-auc:0.858605\n",
      "[85]\ttrain-auc:0.863853\tval-auc:0.858587\n",
      "[86]\ttrain-auc:0.863978\tval-auc:0.858654\n",
      "[87]\ttrain-auc:0.863908\tval-auc:0.858502\n",
      "[88]\ttrain-auc:0.863909\tval-auc:0.858463\n",
      "[89]\ttrain-auc:0.863825\tval-auc:0.858336\n",
      "[90]\ttrain-auc:0.863851\tval-auc:0.858328\n",
      "[91]\ttrain-auc:0.863787\tval-auc:0.858269\n",
      "[92]\ttrain-auc:0.864028\tval-auc:0.858613\n",
      "[93]\ttrain-auc:0.863991\tval-auc:0.8585\n",
      "[94]\ttrain-auc:0.86392\tval-auc:0.858379\n",
      "[95]\ttrain-auc:0.863948\tval-auc:0.858361\n",
      "[96]\ttrain-auc:0.863889\tval-auc:0.858216\n",
      "[97]\ttrain-auc:0.863814\tval-auc:0.858141\n",
      "[98]\ttrain-auc:0.86406\tval-auc:0.858429\n",
      "[99]\ttrain-auc:0.864233\tval-auc:0.858616\n",
      "[100]\ttrain-auc:0.864411\tval-auc:0.858887\n",
      "[101]\ttrain-auc:0.864359\tval-auc:0.858759\n",
      "[102]\ttrain-auc:0.86447\tval-auc:0.858916\n",
      "[103]\ttrain-auc:0.864475\tval-auc:0.858917\n",
      "[104]\ttrain-auc:0.864437\tval-auc:0.858828\n",
      "[105]\ttrain-auc:0.864342\tval-auc:0.85872\n",
      "Stopping. Best iteration:\n",
      "[75]\ttrain-auc:0.863596\tval-auc:0.858938\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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CxM1xHKfWMmXKFB588EFeeuklioqKKCoqYvz48QwcOJDCwkI6dOjACy+8UFwWYVNSGyNS\nxZjZm5LmAL3M7MH4I/1a3CpZQXBQPjOzeZK2Bj4ys9T2yRhCsvVswvbSADP7JCZ8J3mckOx8dTnN\ne4KwVTSfkGz+BiG/hjjWHZJmEyITzwGj4rmngcclHUdINi+L3sBPCE/LASwzs+6S7iY8nfcJwQFJ\nMQK4M5FsDkBMch5IcOhSyebjSruxmT0lqTnwqiQjOGknJ9Y42feHmMh9f4yYrQb+YGbfpPdN4wJJ\nXYB1hEjXv+JYG3zW8XO+FnhZ0lrC1l9vQqL7uLjez1EyCpW0cb6kywj5YXWijX8ys6kxqvcaYUsu\n3fm7TNIFiXFalDEnzOxHhdIPt0ralvDf6s3AQmBUbBNwq5ktL2s8x3GcmswhhxzC+o2P9XTv3r3M\nawcPHlwFFq1HmQxzNg2SCsxsRYyQTCckMX+Sb7uc2kXr1q1twYIF+TYjr1R2TkRNxNfA1wB8DSC3\nNZA008w65jJerY5I1QCeiU9dbQlc7U6U4ziO49Qs3JHKI2bWOd82OI7jOI5TcWptsrnjOI7jbEr6\n9OlD06ZNKSwsWUrwtttuo02bNrRr144BAwYA8NBDDxUnTRcVFVGnTh1mzSr12RqnmuKOlFMtkWSS\nhiWO+8eE7soaP1cNwLaSVsX38yXdGRPNy3u/C2JJjtTxEklz42u+pGtikr3jODWU3r1789xzz5Vo\nmzhxIuPGjWP27NnMmzeP/v2D9OZJJ53ErFmzmDVrFg8++CC77bYbRUVF+TDb2UjckXKqKz8Av1FC\nvLmyUEkNwDaE+l8PS9opdklpABYRJG/ei+87ECrRV6Ru1AWE4qdJuphZe0IF9lZAphpnjuPUEDp1\n6sT225cUTLjjjjsYOHAgW221FQBNmzbd4LpHHnmEE08sVenJqcZ4jpRTXVlDqMx9ITAoeUKla+Nd\nSShB0J5QeX4ucD6hCnkPM3uP8mkAFt/bzNZIehXYI9aoGkcoIloPuMzMxklqFO/bglCN/GpgR4Lm\n4URJX5hZl+R84pObfYGlsbZVB4JMz9Fxfn8HZpjZCGXR3yttIV1rr+bpi1UFvgZVtwaZ9OBSLFy4\nkMmTJzNo0CDq16/P0KFD2X///Uv0efTRRxk3rtRqMk41xiNSTnVmOEHLbttyXLMPIcLUllDUdC8z\nOwC4h/V1t7JpALYzs3sI2oQXm9lJyQ5xa+4wgnP2PdDTzPYFugDDFIpW/ZpQq2sfMysEnjOzWwk6\nel3SnagUZvY/YDFByiYjsdjnbcAJZrYfQZvx2hzWxHGcPLFmzRq++uorpk6dyo033sjvfve7EvWQ\npk2bRsOGDTfIq3JqDh6RcqotZvY/SQ8QKtSvKqt/5PVUhEbSewQtRAjOT0YnJgd2V9DAM2Ccmf0r\nOjV/VRBDXkfQdtwx3meYpOsJUbPJWUfdkLLkg1qTXX+v5EAuWlyCmibUWhX4GlTdGiQFcD/55BO+\n++674raGDRvSqlUrXn45CEH8+OOPjBs3jsaNg9748OHDOfDAAytVRLc0KluwtybiosXO5sbNhKrv\n9yfaStPGS+rWrUscr6NiGoCwPkcqyUkEkev9zGy1pCVAfTNbKGlfgvbfNZImmNlVZU0yVtZvSahc\n3o6S0eJUEnpp+nslSIoWt27d2s496biyLqnVTJo0id95EUJfg02wBkuWLKFRo0bFBR/79OnDsmXL\n6Ny5MwsXLqROnTocd9xxSGLdunWcdNJJTJ48mVatWlWpXSm8IGflr4Fv7TnVGjP7ipBzdEaieQm5\naeNlozwagNnYliAvtDpK1Pw0jrUzsNLMRgE3AvvG/lk1EmO+1e3AWDP7Gngf2FvSVrFg62Gxa0b9\nvXLY7DhOFdKrVy8OOuggFixYQIsWLbj33nvp06cPixYtorCwkBNPPJGRI0cSI8q88sor7LLLLpvM\niXKqBo9IOTWBYUBStjsnbbxslEcDsBQeAp6WNJeQX/VObG8P3ChpHUGL75zY/g/gOUnLEnlSE2Ne\nVR2CtuPV0b6lkh4jaCEuJmgClqa/ly2S5jjOJuSRRx7J2D5q1KiM7Z07d2bq1KlVaZKzCXBHyqmW\nmFlB4v2nJEoHxOOfJ7pfEtsnAZMS/Ton3qefuwO4I8u9eyfeLyHkJaX3+YKEqHOCJcDzGfrfRkgU\nTx23zHTvxPkBwIAM7bOATqVd6ziO42w6fGvPcRzHcRyngrgj5TiO4ziOU0HckXIcx3E2ezLp5A0e\nPJjmzZsX6+GNHz++xDUffPABBQUFDB06dFOb61Qj3JFyag1Rn29U4riupM8lPVPB8RpL+mPiuHO2\nsSRNktSxjPHWRs2+eZJmS/pzWbp9Zdzz0lzm4ThO2WTSyQO48MILizXxunfvXuLcRRddxJFHHrmp\nTHSqKe5IObWJ74BCSQ3icVfgo40YrzHwxzJ75c4qMysys3YE244ErtiI8dyRcpxKIpNOXmmMHTuW\n3XbbjXbtvALJ5o4/tefUNsYDRwGPA72AR4BfAkQdu/sIAsErgbPMbI6kwcCusX1X4OYo6zKE9VXN\nXwSeBQokPU54km8moWxCsd6DpD5ABzO7IB6fCextZhcmjTSzz2L18dfj/evE+3UGtgKGm1lKxHgb\nSc8CewATCc7dX4EG0bZ56XI2SVxrz3XmwNcAMq9BaTp5ALfddhsPPPAAHTt2ZNiwYWy33XasWLGC\n66+/nhdffNG39RyPSDm1jn8CJ0qqTxD/nZY4dyXwppl1IERzHkicawMcARwAXBElYAYSq5qb2cWx\n38+AC4C9CY7XL9Lu/xhwTLwe4HSC87YBZraIIPPSlFBw9Bsz2x/YHzhT0m6x6wEEncC9gd2B35jZ\nQNZHuLI6UY7jVJxzzjmHRYsWMWvWLJo1a8af//xnIOROXXjhhRQUFJQxgrM54BEpp1YRI0wtCdGo\n8WmnDwGOj/1ekvQTSdvEc8+a2Q/AD5I+I+jmZWK6mX0IEKNBLYH/JO6/QtJLwNGS3gbqmdncHEzv\nBnSIBTchVE7fE/gx3nNRvOcjcR6PlzaYa+2VxHXmfA0g8xqUppOXpH379jz88MNMmjSJF154gVGj\nRnHeeeexYsUK6tSpw9KlS+nZs2cVz2Djca0919pznFx4ChhK2Cb7SY7XJDX61pL9v41c+t1DiHi9\nQ0mNwBJIahXH+Iygo3eumT2f1qczQSw5SfrxBrjWXklcZ87XAMpeg3SdvI8//phmzZoBcNNNN3Hg\ngQfSuXNn5syZU3zN4MGDKSgooH///lVpeqXhWnuVvwbuSDm1kfuA5WY2NzoiKSYTxIavju1fmNn/\nUrpXGciqj1caZjZN0i4Enb0OmfpIagLcCfzdzEzS88A5kl6K+n17sT5R/oC4zfc+8HuigwSsllTP\nzFaX10bHcUrSq1cvJk2axBdffEGLFi248sormTRpErNmzUISLVu25K677ip7IGezwx0pp9YRt95u\nzXBqMHCfpDmEZPPTyhjnS0lTJL0F/IuQbJ4rjwFFUYQ4RSo5vB6wBngQ+Fs8dw9hm/CNqL/3OdAj\nnnsd+Dvrk83HxPZ/AHMkveF5Uo6zcWTSyTvjjDMy9CzJ4MGDq8AapybhjpRTa0jq8yXaJhE19szs\nK9Y7J8k+g9OOCxPv/y+t+6TEuX6J953T+h0C3JQ27hal2L6OsB2YXtJgElm09czsEqLOoOM4jpMf\n/Kk9x6lEYhHPhYQn6ibk2x7HcRynavGIlONUIma2HNgr33Y4juM4mwaPSDmO4ziO41QQd6Qcx3Gc\nTUomgeCLL76YNm3a0KFDB3r27Mny5cuBUJKgQYMGxcLBffv2zZfZjpMRd6SqKVGAd1jiuH+UEqmM\nsUckCj9WdIwWksZJelfSe5JukbRl4vwjkuZI+i4K9c6XtCq+nyXpBElXSTp842eU1cZfSXpD0luS\nRkqqG9sl6VZJ/4027pu4ptzCwhWwa0U5+naWdHBl3t9x8k0mgeCuXbvy1ltvMWfOHPbaay+uu+66\n4nO77757sXDwnXfeuanNdZxS8Ryp6ssPwG8kXWdmX+TbmBTRGVkLPAncYWbHSdqC8Cj+tcDFknYC\n9jezPRLXtQSeMbOixHClVufeSDvrACOBw8xsoaSrCOUO7iWIBe8ZXwcCd8R/IcquxDGaAg8D27Bx\n4sIbQ2dgBfBqRQdwrT3XmYP8r0FS065Tp04sWbKkxPlu3boVv//5z3/O449X2f8eHKdS8YhU9WUN\nwTm5MP1EekQpFeGI0YuXY6RokaQhkk6SNF3SXEm7J4Y5XNIMSQslHR2v30LSjZJej5GasxPjTpb0\nFDAf+BXwvZndD2Bma6OdfSQ1BF4AmsfIzi+zTTA5D0lLJF0Xr5khaV9Jz8doV9/ENRcn7LsytjWS\n9GyMIL0l6feEiuY/mtnCeOmLRHkY4DjgAQtMBRpLapZun5l9RpBZ6RejWC3jOrwRXwfH+z8gqbis\ngqSHJB0nqV1c+1nR3j1LWYtjJE2T9Kakf0vaMTqffYELU2spqYmkJ+IavC4pXevPcWo89913H0ce\neWTx8eLFiykqKuLQQw9l8uTJebTMcTbEI1LVm+GEgos3lOOafYC2wFfAIuAeMztA0vkE4dsLYr+W\nBDHc3YGJkvYATiUK50raCpgi6YXYf1+g0MwWSzoPmJm8aawQ/gGhaOSxbBh9yoUPzKxI0k3ACIIg\ncH3gLeBOSd0IUaQDCJIqT0nqBDQBlpnZUQCStgX+B9SV1NHMZgAnALvE+zQHlibu+2Fs+zjdIDNb\nFCNuTQlSLl3N7PvoFD0CdCREuS4ExsZ7H0yIft0E3GJmDylse2atI0XQ6/t5rHL+B2CAmf1Z0p3A\nCjMbGuf2MHCTmf1H0q7A84TPuwRyrb0SuM5c/tcgXdssm67dqFGjWL58Oc2bN2fSpEn8+OOPPPzw\nw2y77bYsWLCA448/nvvvv59GjRqV2wbXmfM1ANfa26yIzskDwHnAqhwve93MPgaQ9B4hOgQwF+iS\n6PdYLAL5rqRFQBvKFs5dvFETKpunErYWmNm3wLeSfpDUONrXDXgz9iuI9k0Ghkm6nuDATQaQdCJw\nU3QKXyBsSW4M9YC/SyqKY+0FYGYvS7pdQfbleOAJM1sj6TVgkKQWwJNm9m4pY7cAHo2RsS2BbGt9\nOLC31svabCOpwMxK5F251l5JXGeu+q1Buq4dwIgRI5g3bx4TJkygYcOGG1zTuXNnHnnkEXbccUc6\nduxY7nu6zpyvAVT+GvjWXvXnZuAMIPnn1xriZxdzgbZMnEuK6q5LHK+jpOOcSQg3JZxbFF+7mVnK\nEfsu0Xc+sF/yYknbALsC/81xXplI2po+j7rRvusS9u1hZvfG7bt9CQ7YNZL+AmBmr5nZL83sAOAV\nILXN9xHro1MQnJiPyIBKCgtfCHxKiPp1pOS6PwCcDJxO0PrDzB4mROdWAeMl/aqUud9G0N1rD5xN\niMRlog4hcpVag+bpTpTj1ESee+45brjhBp566qkSTtTnn3/O2rXhb6BFixbx7rvv0qpVq3yZ6Tgb\n4I5UNSfKmjxGcKZSLGG9I3MsIVJSXn4rqU7Mm2oFLCBsE50jqR6ApL0kZYqfTwAaSjo19tsCGAaM\nMLOVFbAlV54n5GEVxPs2l9RU0s7ASjMbBdxIcKpSyeLEiNQlBJFgCJGvU2Pe088J25kbbOspTViY\nEKH7OEbyTqHkVt0I4rapmc2P17cCFpnZrcA4sggYR7ZlvTOX1ABMF05+gbBFm7KxvNunjpN3evXq\nxUEHHcSCBQto0aIF9957L/369ePbb7+la9euJcocvPLKK3To0IGioiJOOOEE7rzzTrbffvs8z8Bx\n1uNbezWDYUC/xPHdwDhJs4HnKBktypUPgOmEJ9L6xryf0oRzi4l5PD2B2yVdTnDIx7OhTlylYmYv\nSGoLvBa3tlYQokB7ADdKWgesBs6Jl1yskEhfh/CE4UuxfTzQnRA9W0mIIqUoTVj4duCJ6ECWWHcz\n+1TS28DYxFi/A06RtBr4BPhrbG8o6cNEv78RBJVHS/oaeAnYLZ57Gnhc0nEEB+o8YLiC8HJdQqTN\nC+s4NYryCAQff/zxHH/88RnPOU51QOEPbcdxNgaFpxXnAvua2Tf5tidJ69atbcGCBfk2I694Xoiv\nAfgagK9NCQT1AAAgAElEQVQB5LYGkmaaWU6JeL615zgbiUJR0beB26qbE+U4juNULb615zgbiZn9\nG/hpvu1wHMdxNj0ekXIcx3HKRSatvNGjR9OuXTvq1KnDjBkzitunT59erJO3zz77MGbMmHyY7DhV\nRpU5UnKtuPLYUj9WwJ6toPF2ZeLc9pJejHa+KGm7LGMMlvRRwr7usX1LSfcrVDafLalz4polsX1u\nnN81krI9dl/RuaXuMUfSCwryMeUdo4ekvRPHIyQtjvNZqFBZvMVG2Nhb0jpJHRJtbylUFi/tuksT\n72+SdEHi+PmYvJ86Hibpoora6DjViUxaeYWFhTz55JN06tRpg/YZM2Ywa9YsnnvuOc4++2zWrNm8\ni6M6tYuqjEiltOJ2qMJ7lBtJdeMTaU8CY81sT0JhxQKCVhxarxXXwcwaxQrd3YH3EvV7Hjezv8Rt\nnY3lB+BXZrYPUAT8Oj6WDzAQmBDtnBCPs3FTwr7xse1MgFifqCuhcGXyc+8Szx1AKINwVyXMJ50u\nZtYBmEHFnuzrAeyd1nZxXK/WhAKdLyUd4QrwITConNck5zKFUNE8VdtrB6Bd4vzB5KiXpyiu7DjV\nlU6dOm1QgqBt27a0bt16g74NGzakbt3wlf7+++/R+mKyjlMrqMr/YSe14kr8QEkaQahA/Xg8XmFm\nBTFaciWwHGhPqJ80FzgfaAD0MLP34jCHSxpIeHz/IjN7JtYzGkIQet0KGG5md8Vxrwa+JlTwPoc0\nrThJFwKLJV1BQiuOUKAyo7hTch6SlhAkQ46Mcz8LuI74aL6Z3RmvuZjwWPxWwBgzuyLWKEoVVawX\nX6nHKY+L84EgwjuJUBMpV/YmPE6PmX0maTmhmOT0ZCczW6GgabdU0vaEaubjgO2iPZeZ2TgF8d+v\nzOzmOJ9rCcUqHwMeJXwedYFzMqzbK4TH95F0B7A/4XN93MyuiO1DCLWx1hA+hyfj8aGSLmO9Xl7K\nbiNUL+9JWPtxqe9THO8E4Ggz6631daF2jZdfYGZT4vtngE6SWptZiUfcJPUiOE0CnjWzS6KdqVIJ\n84CLCZIwEByot4BmMYK4kiDjkiorcUO01YBrzOzR9O+opJ/FNW1BqFd1dey3H6FcQgHwBdA7Uw2s\nJC5anH/B3upAZaxBUni4PEybNo0+ffrw/vvv8+CDDxY7Vo5TG6jqb7NrxeWgFWdmr0QncGa8/3Az\nmxbH3DHxQ/kJsGMp9z9XocbRDODPZvY1MBs4VtIjhGre+8V/p6dfHNdgcbRxJtAztu0ATFUQLb6P\n4NzcHCMvJ8b59AaeN7Nr41w21HeAowmOMcAgM/sq9p0Qt9U+AnoCbWKtqsZmtjzeN+l4Z5r7GwQn\neVwp63ML2XXq1hEcnEtJFMRUKPZ5fVy3r4EXJPUws4GS+iW/I5LWxHEPBl4j6PcdBHwDzDWzHyUd\nT4g67kOIWr0u6ZU4RPI7ejxp+oEKhVJvA44zs88VxJmvBfqkT1SutVeCfOvMVQcqYw2S+mTZtPKW\nL1/OzJkzWbGiZMH94cOH8/7773PppZfSqFEjttxyYwLIFcN15nwNoIZp7blWXM5aca+Y2VqgKPYb\nI6nQzN5KDh6di2yFv+4gRDQs/juM8AN7H8FZmAG8T9heKk1zTol//6ogCryO4BTsaGZLJH0ZIyY7\nAm+a2ZeSXgfuiz/2Y81sVmLMiZLWAnOAy2Lb7+KPfV2gGSFyNh/4HrhX0jOEKFGu5LJfkFGnLnH+\nYYI23m6Jtv2BSWb2OYCkh4BOlCy8meJVghN1MCFq1Dy+/4aw9QdwCPBI/Lw/lfRyvMf/KPkdnUua\nfqCkQqAQeDHOYQsyCC2Da+2lU9105vJBZa9BJq08gMaNG7Pffvtl1cIbOXIk22+/fYW08jYWr6Hk\nawCVvwabIr56MyFacH+iraq14p5PnojbJulacSek9UlqxTUtY07ZyFUrLmseUozATAR+TYhkfSqp\nmZl9rCBo+1m0937gZ4SoRXcz+zQxl7uJToiZrSFsr6bOvcp6zbkSSNqaEOlbCJwENAH2M7PVcesy\nlYh+DyECtRPrdeVeiU7XUcAISX8zswdi/y5m9kXiPrsB/Ql5aF/HLdL6FoR+DwAOI3w+/YDS9OmS\n/IyQQwYlvxvJ5PmUTt33afMmzmGNwgMS5dk6TZLKk2pP+OyWAn8mOEn3l3JdimSl9IWS9iXk5l0j\naQIwBphnZgdV0D7H2eQsXryYXXbZhbp16/L+++/zzjvv0LJly3yb5TiVRpWXP3CtuBJk04prEiNR\nSGpASAp/J17zFOu3mk4jbl2Z2ekxqTz1dF6zxH16En7IkdQwtQaSugJrUlpwSaJNtxOiSV8Tonmf\nRSeqCyXrJI0hOHr7xzkh6afAp2Z2N8HR2reUddiG4DR8I2lHQr5QyoZtY6L8hYTtL9hQby5pt+JW\nbTOCbAsE57NtdNJ7JrrnolM3ghC5ahKPpxPys3aI35NewMvx3OrUdy3yKmH78iszWxu/+40J23up\nRPPJwO8lbRFztjqRYZtVmfUDFwBNJB0U+9ST1C79WsepajJp5Y0ZM4YWLVrw2muvcdRRR3HEEUcA\n8J///Id99tmHoqIievbsye23384OO1SrZ5AcZ6PYVBl/rhVHqVpxjYCR8Ye6DmHbMrWtNQR4TNIZ\nhK2532UZ/oboGBjBUT07tjcFnlfQofuIILabZGJcqzoEB+nq2P4Q8LSkuYRtwZRjR8z1mQgsj1tU\nEBLiL1bQlVtByFfLtg6zJb0Zx1zK+m2vrQnfi/qE6F2qXMA/gbujw5SKJN4YP7uGwFRC1OvHeG4g\nISL3ebQ9tX1Xpk5dnNuthHwqYiRwIDCR9cnmqTysfxByAN8ws5MI23E7ELYIU6S2eVMRuTEEx2o2\n4bMaYGafSGqTtkztSdMPjLadANwqads4h5sJye6Os8nIpJUH0LNnzw3aTjnlFE45Jf1/O45Te3Ct\nPafcxEjPG8BvzezdfNvjlI5r7XleCPgagK8B+BqAa+05eUahMOZ/CbWt3IlyHMdxNmu8mIdTLmJ+\nVat82+E4juM41QGPSDmO42xmZNLK++qrr+jatSt77rknXbt25euvvwbgyy+/pEuXLhQUFNCvX79s\nQzrOZos7Uo7jOJsZmbTyhgwZwmGHHca7777LYYcdxpAhQwCoX78+V199NUOHDs2HqY5T7XFHynEc\nZzMjk1beuHHjOO20UGnltNNOY+zYUHO2UaNGHHLIIdSvX6l65o5Ta/AcKcfJM5LGEmR76gO3mNk/\nYrmLSwi6k7OBH8ysn0rXC8yIa+251h6ENehcyvlPP/2UZs1CObqddtqJTz/9tJTejuOkcEfKcfJP\nn6g72ICgvfcscDmhCOe3BNHp2bFvaXqBxbjWXklcay+sQWlaeWvWrClxfu3atSWO33nnHT766KMa\nrdPmOnO+BlDDtPYcx8mJ82KBWAiRqVOAl2NldCSNBvaK5zPqBZpZCYVY19oriWvtbVg7J10rr3nz\n5rRu3ZpmzZrx8ccfs/POO2/Qf8WKFTW6BpHXUPI1gMpfA8+Rcpw8EnUgDwcOMrN9CILW75RySUov\nsCi+mqc7UY5TEY499lhGjhwJBGHh447bvJ1vx8kVd6QcJ79sC3xtZiujTMzPCZJBh0raTlJd4PhE\n/1z0Ah2nVDJp5Q0cOJAXX3yRPffck3//+98MHDiwuH/Lli256KKLGDFiBC1atGD+/A3kOh1ns8W3\n9hwnvzwH9JX0NkGUeCpBE/GvBC3JrwgRqm9i/zL1Ah2nLLJp5U2YMCFj+5IlS6rQGsep2bgj5Th5\nxMx+AI5Mb5c0Iz69V5cgdDw29v8C+P2mtdJxHMfJhm/tOU71ZLCkWcBbwGKiI+U4juNULzwi5TjV\nEDPrn28bHMdxnLLxiJTjOE4t5JZbbqGwsJB27dpx8803AzB48GCaN29OUVERRUVFjB8/Ps9WOk7N\nxyNSjlPDkbSFma3Ntx1O9eGtt97i7rvvZvr06Wy55Zb8+te/pkmTJgBceOGF9O/vAU/HqSw8IuU4\n5UDSWEkzJc2L1cORdIakhZKmS7pb0t9jexNJT0h6Pb5+Ucq4h0qaFV9vStpagb9LWiDp35LGSzoh\n9l8i6XpJbwC/3SSTd2oMb7/9NgceeCANGzakbt26HHroobzyyiv5NstxaiUekXKc8lHpci6R/sCf\nzGyKpALge6An0BrYG9gRmA/cl7jmSzPbtyyDXWtv89HaWzLkKAAKCwsZNGgQX375JQ0aNGD8+PE0\na9aMZs2acdttt/HAAw/QsWNHhg0bxnbbbZdnqx2nZuOOlOOUj0qXc4lMAf4m6SHgSTP7UFIn4JG4\nbbdM0ktp1zyazUjX2ivJ5qK1l9QPO+644zjooINo0KABLVu2ZO3atXTo0IH77rsPSdx333383//9\nH5dcckn+DN7EuM6crwG41p7j5I00OZeVkiYRimVmizKl5Fy+L2tsMxsSo1vdgSmSjsjBpO9KGc+1\n9hJsjlp7nTt35sYbbwTg0ksvZeXKlfzmN78pPt+qVSuOPvrozUp3zXXmfA3AtfYcJ59UmZyLpN3N\nbK6ZXQ+8DrQhVC3/vaQtJDUDulT+lJzaymeffQbABx98wJNPPsnhhx/Oxx9/XHx+zJgxFBYW5ss8\nx6k1eETKcXKnKuVcLpDUBVgHzAP+BfwI/IqQG/UB8FoVzMmppRx//PF8+eWX1KtXj+HDh7PFFlsw\nYMAAZs2ahSRatmzJXXfdlW8zHafG446U4+RIVcq5mNm5WU71S9xnRKJ/y5wNdzZLJk+eXOJ40qRJ\nPPjgg3myxnFqL+Xe2otbGB2qwhjHqaG4nIvjOM5mSk4RqZhUe2zsPxP4TNIUM7uoCm1znBpBeeRc\nJJ0OnJ/WPMXM/pTDfXqX0zTHcRynisl1a29bM/ufpD8AD5jZFTHvw3GccmBm9wP359sOx3Ecp3LI\ndWuvbnxq6HfAM1Voj+M4jlNOMunqjR49mnbt2lGnTh1mzJiRZwsdp/aSqyN1FaEq83tm9rqkVsC7\nVWeWszkiySSNShzXlfS5pAo575IaS/pj4rhztrEkTZLUsYzx1kYJl9mS3pB0cA42ZCq+6TiVRlJX\nb/bs2TzzzDP897//pbCwkCeffJJOnTrl20THqdXk5EiZ2Wgz62Bm58TjRWZ2fFnXOU45+Q4ojPIr\nAF0J5QUqSmPgj2X2yp1VZlZkZvsA/w+4rhLHdpwKkUlX78knn6Rt27a0bt063+Y5Tq0n12TzvYA7\ngB3NrDA+tXesmV1TpdY5myPjgaOAx4FewCPALwEkbU/QmmsFrATOMrM5kgYDu8b2XYGbzexWYAiw\ne3yi7kXgWaBA0uNAIeHBiZPNzFI3l9QH6GBmF8TjM4G9zezCNDu3Ab6OfQqAccB2QD3gMjMbl+yc\nrY+kloSaUf8BDiY4jseZ2SpJewB3Ak2AtcBvzew9SRcTttm3AsaY2RWlLahr7dVOrb3SdPU6diw1\nuOo4TiWSa7L53cDFwF0A8cfrYcAdKaey+Sfwl7gF14HgOP0ynrsSeNPMekj6FfAAkKoW3oZQ+Xtr\nYIGkO4CBQKGZFUGxxMvPgHbAMoK+3S8ITkyKx4BBki42s9XA6cDZ8VyD6JTVB5oRimVCFBiOD2Ts\nAEyV9FTSQcvWJ57bE+hlZmdKeoxQHX0U8BAwxMzGSKoP1JHULfY/ABDwlKROZvZKchFda68ktVFr\nrzRdvY8//rj4/PLly5k5cybNmzd3jTXXmfM1IH9aew3NbHpCfBWgdv1fyakWRCe9JSEaNT7t9CFE\nCRYze0nSTyRtE889Gwtm/iDpM2DHLLeYbmYfAkSnqCUJR8rMVkRx4KNjBfN6ZjY3nl6VcMoOAh6Q\nVEhwaP4aRYbXAc3j/T9J3DdbH4DFZjYrvp8JtJS0NdDczMZEu76P9+0GdAPejP0LCI5VCUfKtfZK\nUtu19tJ19Vq0aFGsJda4cWP2228/VqxY4RprrjPna0Dlr0GujtQXknYHDEDSCcDHpV/iOBXmKWAo\n0Bn4SY7X/JB4v5bs3+1c+t0DXEqQe8lYqsDMXouRpSYEoeEmwH5mtlrSEkLUKslJpfRJt6kB2RFw\nnZm5todTzGeffUbTpk2LdfWmTp2ab5McZ7MhV0fqT4S/bttI+ohQvfmkKrPK2dy5D1huZnPjdlyK\nyYTv3dWx/Yu4VZZtnG8JW33lwsymSdoF2JewvbgBUbR4C+BLgpjxZ9FB6gL8NMMlufRJ2vCtpA8l\n9TCzsZK2ivd7njD/h2L0rDmw2sw+K+88ndpDuq5e48aNGTNmDOeeey6ff/45Rx11FLvuuiuvv/56\nvk11nFpHmY6UpDpARzM7XFIjoI6ZfVv1pjmbK3Hr7dYMpwYD98VisCuB08oY50tJUyS9RUjoLk+2\n8WNAkZl9nWhL5UhBiAydZmZrJT0EPC1pLjCDEMlKJ5c+6ZwC3CXpKmA1Idn8BUltgdeiA7kCOBlw\nR2ozJl1XD6Bnz5707Nmz+Hhzz4txnKqiTEfKzNZJGgA8ZmbfbQKbnM0UMyvI0DYJmBTffwX0yNBn\ncNpxYeL9/6V1n5Q41y/xvnNav0OAm9LG3SKL3V8AB2U5V1BWH8IThKn+QxPv32V9QntyzFuAW7KM\n5TiO42xCci3I+W9J/SXtImn71KtKLXOcPBCLeC4kJJZPyLc9juM4TvUm1xyp38d/k8KqRqjb4zi1\nBjNbDuyVbzscx3GcmkGulc13y/ByJ8pxHCdP3HTTTbRr147CwkJ69erF999/z+zZsznooINo3749\nxxxzDP/73//ybabj1HpycqQknZrpVdXG1SSiTtywxHH/WHG7MsYeEUtObMwYKZ24tyQ9LanxRoy1\nJD76nxw39RpYynU9JO2dw/i59hssqX/5rN94JB0iabqkd+LrrFL69pb09wzt4zfmM3A2bz766CNu\nvfVWZsyYwVtvvcXatWv55z//yR/+8AeGDBnC3Llz6dmzZ3FtKcdxqo5cc6T2T7x+SXh66tgqsqmm\n8gPwm5SDUV2QlNq+TenEFQJfUXKbdmNIjZt6DSmlbw+gTAepHP02OZJ2Ah4G+ppZG0JS+tmSjsrQ\nN+vWuZl1j9uIjlMh1qxZw6pVq1izZg0rV65k5513ZuHChcUixV27duWJJ57Is5WOU/vJKUfKzM5N\nHse/pP9ZJRbVXNYQam1dCAxKnpA0AnjGzB6PxyvMrCDWQroSWA60JzxyPxc4n1CUsYeZvReHOTxG\ne7YBLjKzZyRtQdCT60zQXRtuZnfFca8maMG1YcOcn9dI1EfKpt0maSywC6Fw5C2xWnZOSBpCcLbX\nAC8AT8bjQyVdRqhQ/iuCjMmWwH8Jj/sXZegHMJxQ0HIlcKaZZS0fIOkioE88vMfMbi5tPpJWEJ6C\nOxpYRdC6+1TSb4ErCEUyvzGzTgQHdISZvQHhabz4VOtg4Nn4WX9PkKKZAszJYuMSoCOhMnk2rb3d\nM807i11Zca292qO1l9LXa968Of3792fXXXelQYMGdOvWjW7dutGuXTvGjRtHjx49GD16NEuXLs2z\nxY5T+8k12Tyd74DdKtOQWsJwYI6kG8pxzT5AW0KUaBHhh/8ASecD5wIXxH4tCfpquwMTFQRtTyX8\nkO4fCzZOkfRC7L8vQWducfJm0fk6DLg3Hpem3dbHzL6S1AB4XdITZvZlmv3J2koA1wH/BnoCbczM\nJDU2s+UK2nJJh3K5md0d318DnGFmt2XoN4EQAXpX0oHA7WQoCxD77kfQxzswzmeapJfN7M1S5tMI\nmGpmg+JndyZBR/IvwBFm9lFiG64dMDLttjNie4oWwMGxxlTvTHamkU1r7x9Z5p3JrvR1cK29BLVF\nay9VC+rbb79l5MiRjBo1ioKCAgYPHsygQYPo27cv1157LQMGDOAXv/gFderUKb7GNdZ8DcDXAPKk\ntSfpaaI8DGE7cG9gdKVZUUuIVbYfAM4jRDZy4XUz+xhA0nuE6A2EyFSXRL/HzGwd8K6kRYRIUzeg\nQyJ/alvCj/KPBE25pBOVcniaA28DL8b20rTbzpOUqui3S2xPd6SK9edSxC2t74F7FcSHn8ky98Lo\nQDWO930+vYOkAkKkZrTWVzDfKst4ELbaxqRqnkl6krAd/WYp8/kxYeNMoGt8PwUYEZ2bJ0u5Zzqj\nzWxtOfpn0torbd5l2uVaeyWpbVp7o0eP5mc/+xk9eoSyasuWLWPq1KmceuqpnHpqSF9duHAh8+bN\nK9YUc401XwPwNYD8ae0NTbxfA7yfEn51NuBm4A1KarStIeajKVSK3zJxLqmzti5xvI6Sn49REiNE\nXM41sxIOSNzaSy+eusrMiiQ1JDgsfyJUD8+o3RbHOBw4yMxWSprEhvpxGTGzNZIOIES+TgD6kTmC\nNIKwfTk7Rm46Z+hThyAXU5ThXM6UMZ/VZpZa32L9PTPrGyNBRwEzY7RrPrAfMC4x/H7AvMRxeQvX\nZtLayzrvTHZliBQ6tZhdd92VqVOnsnLlSho0aMCECRPo2LFjsebeunXruOaaa+jbt2++TXWcWk+u\nyebdzezl+JpiZh9Kur5KLauhxOrbjwFnJJqXEH5sIeT/1KvA0L+VVCfmzbQCFhAconMk1QOQtJeC\njE9p9q0kRMz+HCNHzwN9YgQESc0lNSVEt76OTkcb4Oe5GhrH2tbMxhNyxvaJp9K177YGPo72J7Ub\ni/uZ2f+AxTEvCAX2ITuTgR6SGsa16Bnbyj0fSbub2TQz+wvwOSGKNRzoLako9vkJcD1Qnu3cMilt\n3lnscjYjDjzwQE444QT23Xdf2rdvz7p16zjrrLN45JFH2GuvvWjTpg0777wzp59+er5NdZxaT64R\nqa7AJWltR2ZocwLDCFGYFHcD4yTNBp6j/BELgA+A6YRk875m9r2kewi5U28o7P98TgYJlXTM7E0F\nvbpeZvagMmu3PQf0lfQ2wWnLJiefniP1HCFxe5yk+oSI10Xx3D+BuyWdR4hUXQ5Mi3ZPY72Tld7v\nJOCOmHxeL56fHfteJimVR4aZtYgJ39Nj0z1xvvNznE+SGyXtGecwAZgdc75OjvZtHc/dbGZPlzJO\nb0nJzyVXpzTbvDewK8fxnFrElVdeyZVXXlmi7fzzz+f888/Pk0WOs3mi9TsaGU5K5wB/JERA3kuc\n2hqYYmYnV615juNsLK1bt7YFCxbk24y84nkhvgbgawC+BpDbGkiaaWYdcxmvrIjUw4RHs68DkoUW\nv41bWI7jOI7jOJstpTpSZvYN8A3QCyDmztQHCiQVmNkHVW+i4ziO4zhO9SRXiZhjJL0LLAZeJiRP\n/6sK7XIcx3ESZNLWA7jtttto06YN7dq1Y8CAAXm20nE2P3J9au8aQoLsQjPbjfBYey7Juk41RFWo\nCxjHO0vrdeimSzokce6XkuYp6PI1kNRO0kuSFkh6V9LlShROqkziPN+J935dG6kXqVARHUk7S0oV\nDy2S1D3Rx7X2nI0mm7bexIkTGTduHLNnz2bevHn077/JpScdZ7MnV0dqdaxTU0dSHTObSJC3cGom\nVaYLKOlo4GzgkKhF1xd4WEGjDsKTaNcl6iM9BQwxs9aEMgkHEx5wqGy7+hKePj0g3vswwlNv6f22\nKO/YZrbMzFJFUYuA7qX1j9e41p5TLjJp691xxx0MHDiQrbYKtVqbNm2aZysdZ/Mj1/IHy2NtoMnA\nQ5I+o2KP8DvVg6rUBbwEuNjMvgAwszckjQT+JOl9gqbfEZKOBF4iPP35Quy7UlI/YBIwPEbJdgf2\nAHYAbkhIymygDyipJVl064BLgc6xPlOqTtPIONYS4FGCo3WDpNfJrHG3G+EBjAISBTnjfZ8hyPJc\nRSgJcQjhIY2MyLX2Nik1VWuvLG29AQMGMHnyZAYNGkT9+vUZOnQo+++/f56tdpzNi1wdqeMIkicX\nECIK2xJ+MJyaS1XpArYjyJwkmQGcZmaXRwfjGTN7XNLf0vua2XuSCiRtE5s6ELaVGwFvSnoWKCSD\nPiCh1tYGunUK2n1bm9miUub2pZntC6Vq+90C3GFmD0j6U/oAZvajpL8AHc2sXxyrd2kLGnGtvSqm\npmrtlaWt98033zB37lyGDBnCO++8w7HHHsvDDz9Mpt1x11jzNQBfA8iT1p6ZfSfpp8CeZjZSQWak\n3FsgTvWhinUBK5NxMaK0StJEgvN0CJn1AT8gg25djvd5FMrU9vsFwckBeJBQ0bwycK29Kqama+1l\n09Zr3bo15557Ll26dKFLly4MHTqUwsJCmjRpssEYXj/I1wB8DaDy1yDXp/bOBB4HUnpszYGxlWaF\nky9uJkjZJGVlNlYXMKVFlyRdi45sfSW1AlaktuDIrjF4nZkVxdceZnZvBhvXAnXjWCvi2NlIbVUX\na9wlXm3T7l/ZbGBzaXaYWV/gMoI0zEwFmRqnFpPU1jMzJkyYQNu2benRowcTJ04Egkjxjz/+yA47\nVHrqo+M4pZBrsvmfCH+Np/JL3gU8q7GGU0W6gDcA16d+3BU06XoTtqXSeQg4RNLhsW8DgpBycrvx\nOEn143idgdfJrg9YGtcR8q62idcUZHpqrwxtvynAifH9SenXRtL1BCuEa+05SbJp6/Xp04dFixZR\nWFjIiSeeyMiRIzNu6zmOU3XkmiP1Q8z/AEBB7LYq/jJ3Nj2VqgtoZk9Jag68KskIjsXJqS3BtL6r\nJB0H3CZpOGG7+EEgWS5gDjCRkGx+tZktA5Ypsz7g2lJMu4OwBfi6pNXA6jj3TGTTuDuf8ATiJSSS\nzdOYCAxU0B9MJZu71p6z0WTS1gMYNWpUHqxxHCdFqVp7xZ1CQvJy4FRCUvEfgflmNqjUCx1nI4hP\n7a0ws6H5tqUm41p7nhcCvgbgawC+BlD5Wnu5bu0NJGwhzCXUCBpPyNFwHMdxHMfZbCl1a0/Srmb2\ngZmtI2z53L1pzHIcMLPB+bbBcRzHcUqjrIhU8ZN5kp6oYlscx3GcLLjWnuNUT8pypJKPf5T26PiG\nFxGJzuYAACAASURBVFahnpukEZJOKLtnqWO0kDROQd/tPUm3SNoycf4RSXMkfaegzTZf0qr4fpak\nEyRdlXribGOR1FjS4wpacG9LOii2by/pxWjni5K2y3J9kaSp0bYZkg6I7VtKul/SXEmzFSqUp65Z\nEtvnxvldI6l+Zcwnwz3mSHpB66ViyjNGD0l7J45HSFoc57NQ0gOSWmyEjb0lrZPUIdH2lkLF8tKu\nuzTx/iZJFySOn5d0T+J4mKSLKmqjs3njWnuOU30py5GyLO9zocr03DYGSXUVHvV6EhhrZnsCexGe\n6Lo29tkJ2N/MOphZo6jN1h14L1HT53Ez+4uZ/buSTLsFeC7q0+0DvB3bBwITop0T4nEmbgCujLb+\nhfUlBM4EMLP2BAmUYQr1oVJ0ief+f3vnHWdVde3x7w9QioDEKAoSM2IBBQFFSJEgGGzRRLCX2J+K\nsSAJlsQo2PKMiKA+xdhAo6JYMcan+GgSLDQpYgQViJ0iCoKDMLDeH3tfOFzuvTNzmeEOM+v7+fCZ\ne/bZZ5+11xy4i7X3Wb/OhGD5b1Q83c2sHaHC+Z9K65yBnsD+aW1Xmll7oBWhMOfYZCCcB5+SJpdT\nBpJzmUQooJmqv7Uzocp7ip8Db5Rl0PhWrONsgmvtOU7VpLR/sNtLWkHITNWPn4nHZmaNs19aqXpu\nAD0kXQM0Bn5vZi8pCM7eSqg3VBe4x8z+Fse9CfgaaA1cDKw2s2GEiayT1JdQt6c/oWL37vEV9svM\nbGKmCSbnoaCdNgI4Os79QsLr73sDA83svnhNJo24HYGuhHpLmNkaYE28zXFxPhC04cYT9OzSsegL\nCBI+n8fP+xM07TCzxZK+IWi8Td7kYrOVCsK+n0jaKd5/FPADwqv3fzazUZJuBJaZ2ZA4n1uAxYTf\n1VPRhjrAxRn89jqhkjqShgKdCL/XZ8ysf2y/lVC/qoTwe3guHh8aywCckBzQwmungyX1Ivh+VOp5\niuOdCBxrZudI2gW4D9gjXn6FmU2Kn18CukpqZWabvOIm6TRC0CTgn2Z2dbSzfnxG5gBXAoPjJW2A\nd4FmMYP4HUFaZ3oM4m+Lthpws5k9lf6MSjow+rQFoSzETbFfR+AOQuC/FDgnU2mJJK6151p7juNU\nHjkDKTPbUhmYytJzgyD90ZkgajtO0t6E8gzLzayTpLrAJEkpGZODgLZmtkDS5Wyu8bZC0seEwOc3\nhACpQznn+7GZdZA0GBhOKGJaj/Clep+kI8isEbeC8FbkMIWii9OAPma2Ctg18UX5JbBrlntfAbwq\n6XZCpvHnsX0m8BtJIwiFGzvGn5PTB4g+WBBtnAb0im07A28paNY9TAhuhsTMy6lxPucAr5rZLTGg\nbZDBxmMJgTHAtWa2LPYdE5fVPgN6Aa3NzCQ1MbNv4n2TgXem+U8nBMnZ6jtByPoNNrN/SdqDUNgz\nVbV8PSHA+RNwduoCSc0JUjAdCUHOaEk9zewaSZcmnxFJJXHcnwNvEhQAfgYsB2bHWmwnAB0Iz/nO\nhLpWr8chks/oCcDnZnZMHHtHSdsBdxNEjZdIOoWQRT0vfaJyrb1NcK0911gD9wG4D6BAWnv5Usl6\nbiPj24QfSJpP+BI9AminjfundiQEBWuAyWa2YIsmVDovJmxtaGbfAt9K+l5BXPYIMmvEzSR8iV5m\nZm9LupOwhHddcvAYXGRbYr0Y6Gtmz0o6GXgI6EEIfPYjLKv9h7C8lKtwpRI//xIDvfWEoGBXM1so\n6auYMdkVeMfMvpI0BXg4ftm/kNCOgxDoriMU10yVzTg5ftnXAZoRMmfvAauBhyS9RMgSlZWylHPu\nAeyf+JJprFgdPfIEcK2kPRNtnYDxZrYEQNLjhOxhJomkNwhB1M8JWaPd4+flhKU/CDqBI8xsHbBI\n0oR4jxVs+ozOJizD/pUQRE6U1JYg2PxanENtIGM2yrX2NsW19rx+ELgPwH0AFe+DrbEXYwghWzAs\n0balem6QXYPtMjN7NXkiLpskK3S/B5yY1qcxYcnnQ/KXv0namj6POmzUiNtkH1Lck/Wpmb0dm55h\n416oRZKamdkXkpoRltGQNAw4kJC1+BUhi9InXvM08CCAmZUQlldT93oDmJfJeEmNCJm+eYSq2rsA\nHc1sbVy6TG1Ef5CQgdqNEKhhZq/HoOsYgqDuHWb2aOzf3cyWJu6zJ9CPsA/t67hEWs/MShQ2yf+S\n8Pu5FDgsk60ZOJCwhww2fTaSm+drAT81s9Vp8ybOoUThBYlMS6dlIbVP6gBCFvIT4A+EIGlYjutS\nbHhGzWyepIMIe/NuljQGeB6YY2Y/y9M+ZxslqbVXv359xowZw8EHH0y7du0YN24c3bt3d609xykQ\nZS3ImTeVpOcGcJKkWpL2ImySnktYqrk4ZkWQtK+kHTJcOwZooKi1FpeXBgHDzey7PGwpKxk14szs\nS8LepFax3y8JwR6ELFdqqels4tKVmZ0bN73/Kp77HDg0fj4M+CDeo0HKB5IOB0rMLDX2BqJN9xKy\nSV8TsnmLYxDVHfhxovvzwFGETMqr8fofA4vM7AFCoHVQDj80JgQNyyXtStgvlLJhRzN7mRD8pTTu\nsurXKXA5Iav1SmxeJGm/GKT3SnQfTVgeTl2bael2OCFzlfov/WTC/qyd43NyGjAhnlubetYibxCW\nL5eZ2br47DchLO+lNppPBE6RVDvu2epKhmXWuKT4nZk9Bgwk+HMusIs2vtG5naQ26dc61Q/X2nOc\nqsvWejuoQvXcIh8TvoAaA73NbLXC6+ZFbNzUu4TwxtcmxCWyXsC9kq4jBJQvk98bZWXGzEYrs0bc\nYsIX/OMKb57NB86Nl90KjJR0PmFp7uQsw18A3Knwxtdq4v4YQnbtVUnrCXuQzky7blz0VS1CgHRT\nbH8c+Iek2YRlwfcT81gjaRzwTVyigrAh/koFHbuVhP1q2fwwU9I7ccxP2Ljs1YjwXNQjZO9S5QKe\nBB6IAVMqkzgw/u4aAG8Rsl6pDfrXEJYFl0TbU8t3lxOEi2cRnv3Xgd5ptq2RdBdhPxUxE3gNQUMv\ntdk8tQ/rfsIewOlmdgZhOW5nwhJhitQybyoj9zwhsJpJyJxdZWZfSmqd5qYD4hzXE3QBL462nQjc\npfCCQh1CxndOZk871QnX2nOcqkmZtPYcJ0nM9EwHTjKzDwptj5Mb19rzfSHgPgD3AbgPoHBae44D\ngEJhzA8Jta08iHIcx3FqNF74zykXcX9VuarcO47jOE51xTNSjuM4VZhsGnsAgwYNQhJLly7NMYLj\nOJWJB1JOjUfSOgWNwjkK+n1/0KYyOpmu6RZrXWU696e049T470r6R6wplmvsJpJ+lzhuLumZ8szJ\nqR5k09gD+OSTTxg9ejR77LFHKaM4jlOZeCDlOFAcS0m0IegRHg3034Lx0t/+TI3fllCx/5JSrm8C\nbAikzOxzM9sikW5n2yWTxh5A3759ue2227zcgeMUGN8j5TgJoh7hhQTplgGE/2xspt8YuzeW9E+C\nrNA4QvDzFxIafLEsQpI3gXawoW7WZnqG8X57xTFeI0gtvWRmbWNpiKEEvcQSgs7kuFxzcq29bU9r\nrzSNvVGjRrH77rvTvn37UkZyHKey8UDKcdIws/mx+GZTgmh0Nv3GzgRpm/8Q6qEdn0mDL0Uc85cE\n+R4I9b4y6RleQ9Dc6xCvK0oMc0kw0Q6ItadGS9o3Q7V219pLsK1p7eXS2PvjH//ICy+8wMCBAxk/\nfjyrV69m0qRJ7LjjjjnHdI019wG4D2Ab09pznGpAafqN8wEURKG7EOR90kllqHYH/k3IMkEWPcNS\n7OlCEC7GzN6X9B9gX4KO4QaSWnt7tNzbBs2u2X/V/3BACduSDxae0Q3IrLE3bNgwvvrqKy69NNQ4\nXrp0KZdddhmTJ09mt912yzqm1w9yH4D7ALZNrT3H2aaQ1JIg7LyY3PqNmfQeM1FsZh0kNSBI6lwC\n3EVuPcMKo/52tZkbl4pqKuPHj98QnGxLZNLYO/744xk3buNqblFREVOnTnWNPccpEL7Z3HESRP27\n+4D/sVD2P5d+Y2dJe8Y3/E4B/hXb0zX4AIg6jpcDf4hSPtn0DLNqCxK0+s5I2UIQ2q7ZZcurMdk0\n9hzHqTp4RspxNi69bUfYwP134I54Lpd+4xTgf9i42fz52J6uwbcBM3snav2dRhY9QzP7StIkSe8C\n/0vYbJ7iXmBovKYEOMfMvq8YNzhVkWwaeykWLly49YxxHGczPJByajxmVjvHufWEcgbpJQ3GA12z\nXHM1cHXiuGHa+V8nDn+WZYzT05raxvbVbBS0dhzHcQqML+05juM4juPkiQdSjuM4juM4eeJLe47j\nOFkoKiqiUaNG1K5dmzp16jB16lRmzpxJ7969WblyJUVFRTz++OM0bty40KY6jlMgalRGSpJJGpQ4\n7herV1fE2MMTtYbyHaOFpFGSPpD0kaQ7JW2fOD9C0ixJfRX4c+w7T9I4SW22fCYZ7dpX0svxXtMl\njZRUWr2jXOMNkNQvfr5RUo/4+YpYIiDVb2EsVJm89jeSrsn33jls2lnSWkm987x+ZfxZFDeJO9WE\ncePGMWPGDKZOnQrAf/3Xf3Hrrbcye/ZsevXqxcCBAwtsoeM4haRGBVLA98Dx6V/OhUZSnfhG2HPA\nC2a2D6HIYkPglthnN6CTmbUzs8GEWkQ/B9qb2b7AfwMvRgmRirStHvBPYKiZ7WNmBxHeHNslfQ75\njG9m15vZ/8XDK4AGpfR/0cxuzedepXAS8BbhbTrHycq8efPo2jW8Z3D44Yfz7LPPFtgix3EKSU1b\n2ishvJreF7g2eULScIKe2TPxeKWZNYyFF28AvgEOAEYCs4E+QH2gp5l9FIfpEbMljQkaaC9FWZDN\ntNriuDcBXwOtgYuB1WY2DMDM1knqCyyQ1B8YDeweX9O/jPBW2KGxNhFmNlrSG4QaQw/FDMkDhMrc\nXwKnmtkSSXsRXqffBfgOuCBWyB4OrCBouO0GXBV9cTrwppn9I+UrMxsffXQOcDwh4KsNHCrpSuDk\nONfnzax/7HstcDahyOUnwLSk34Hm8c84SUvNrHumX2C858FmdmkOm8lkR6z/NBJoEe29ycyeikOf\nBvwBeEJSCzP7NPUcAHcCxwLFwHFmtkjSnsATce6jMtmaZncHQn2qBsBHwHlm9rWkCwhSLtsDHwJn\nmtl32eYmqRnwFOEZqwNcbGYTc93btfbKp7W3MFG8VBI9evSgdu3aXHTRRVx44YW0adOGUaNG0bNn\nT55++mk++eSTyjLbcZxtgJoWSEEIImZJuq0c17QH9gOWAfOBB82ss6Q+hKDmitiviKC/thchINgb\nOIvsWm0HETTVFki6nBhcpIgabB8T6hT9hhDodZDUGNghJU+SYCqQWt7bAZhqZn0lXQ/0By4lBJK9\nzewDST8hZJcOi9c0I0iQtAZeJMidtE23K42DgHZmtkzSEQT5lM6EiuAvRvmTVcCpQAfCMzc9w1zv\nkvR7oLuZLc1xv3Q2szmHHbsAn5vZMQCSdow/fwQ0M7PJkkYSimumloB3AN4ys2vjM3MBcDMhuBpq\nZo9KuqQMdj5KqJA+QdKNhN/HFcBzZvZAtONm4HyiBEymuREC21fN7JYYpGfM4Mm19jahPFp7SQ2u\n2267jV122YWvv/6afv36UVxcTO/evbnlllu46qqrOOSQQ6hVq9Y2oV3mGmvuA3AfgGvtbTExOHmU\nUGG6uIyXTTGzLwAkfUTIDkHITCUzJyNj3aEPJM0nfAGWptW2YIsmlJ31hMwFwGPAc5IaEpYDnw4r\niUDI2KR4Idr/Xjn2QL1mZsvi5yPin3ficUPCXBsRskLfASgI81YUmWzOZsdEYJCkvxKC0lQm5xRC\npgrgSeBhNgZSawgZMwjB3+Hx8yHACfHz34G/ZjMwBmxNzGxCbHoEeDp+bhsDqCbRzqQUTaa5TQEe\nVqic/oKZzch0T9fa25TyaO1lk5KZOXMma9eu5ayzzuKss84CwjLfnDlztgntMtdYcx+A+wBca6+i\nGELIigxLtJUQ94wpSH5snziXrBy9PnG8nk19mEl7LZdW26pE03vAiWl9GhMkQD4Emm4YNASDqyS1\nTMtKdQQmkBmL8/vGzDpk6ZOcZyrSmgMcmqU/aXMQ8N9m9re0eVxB5ZHJ5ox2RFsOAn4F3CxpjJnd\nSFjW201Sqgp5c0n7mNkHwNooFQNBfy/X7zsfhhOWh2fGZctuiXObzc3MXo/ZtWOA4ZLuMLNHc93A\ntfby09pbtWoV69evp1GjRqxatYrRo0dz/fXXs3jxYpo2bcr69eu5+eab6d07r/cTHMepJtS0zeYA\nxAzKSMIySoqFhEAEwjLaZlppZeAkSbXiPqSWBA20XFptScYADSSdFfvVJmRFhqcyOWkMBO6SVD/2\n70FYBnoinq/FxsDsdOBfZraCsOfqpHiNJLUvZU5PAD+XtOGbWFJXSW0z9H0VOC9mvpC0u6SmwOtA\nT0n1JTUCfp3hWsitMVceMtohqTnwnZk9RvDfQQp6dQ3NbHczKzKzIsLG/dI2nU8iLFdC1L7Lhpkt\nB76W9IvYdCYbA95GwBfx+cg5TpzLj4FFcTnwQcLSqlMJLFq0iC5dutC+fXs6d+7MMcccw1FHHcWI\nESPYd999ad26Nc2bN+fcc73QvOPUZGpqRgpCkHJp4vgBYJSkmcArbJppKSsfA5MJG4F7m9lqSbm0\n2jZgZiapF3CvpOsIgdDLbC5NkuJu4AfAbEnrCBvKjzOz1HLlKoKo7p8JG7xPie1nELTa/kwIFp8E\nZmabkJkVSzoWGCJpCLAWmEXYbJ/ed7Sk/YA349LhSuC3ZjZd0lPxPosJy1OZuB94RdLnic3msySt\nj59HxnvnJJsdhL1mA+N4awkb/E9jo0ZeimcJy6I35rhNH8LG9KvZfLN5K0mfJo77Ejba36dQ3mE+\nG2VergPeJjwXb1N6INkNuFLS2jivs0rp7+RJy5YtmTlz878affr0oU+fzR5/x3FqKNq4auFUJxTf\nOiy0HU7hadWqlc2dO7fQZhQU3xfiPgD3AbgPoGw+kDTNzA4uy3g1cmnPcRzHcRynIvBAqpri2SjH\ncRzHqXxq8h4px3GcrGTS2ZsxYwa9e/dm9erV1KlTh3vvvZfOnTsX2lTHcQqIZ6ScKomCLuJjieM6\nkpZIeinXdTnGayLpd4njbtnGkjReUs618Vjx3KnmpOvsXXXVVfTv358ZM2Zw4403ctVVVxXYQsdx\nCo0HUk5VZRWhWGX9eHw48NkWjNcE+F2pvRwnB5JYsWIFAMuXL6d58+YFtshxnELjS3tOVeZlQuHJ\nZwhlCkYAvwCQtBOhAnlLgmbghWY2S9IAQhHTlvHnEDO7i6B3uJeCVuFrBCHmhpKSMji/TRTfRNJ5\nBPmbK+LxBcD+ZtY30acbMABYmj6OpE4EKZkdCMU1f0kouzCUoKFXQtBkHBeLcfaMffcBbicUhT0z\nXvurKMOTUSsxlxNda6/sWnul6ewNGTKEI488kn79+rF+/XreeOONyjTbcZxtAA+knKrMk8D1cQmu\nHSFwShW1vAF4x8x6SjqMoGWXqtjemiDd0wiYK2kocA1B17ADbAiADiRoE35OKLB5CPCvxP1HAtdK\nutLM1hJqP12Uwc7NxpE0mVCL6hQzmxKr1BcT6k+ZmR0gqTUwOhYFhRCIHQjUI1Szv9rMDpQ0mFAv\nagi5tRI34Fp7m1JWrb3SdPYmTJjA+eefz6GHHsq4ceM4/vjjGTRoUPYBqxCuseY+APcBuNaeU4OI\nGaYiQjbq5bTTXYhad2Y2VtIPY7AC8E8z+x74XtJiIJtu4GQz+xQgZqqKSARSZrZS0ljgWEn/BrYz\ns9llHGc58IWZTYljrYjnuxBFic3sfUn/AVKB1Dgz+xb4VtJy4B+xfTZBr7E0rcQNuNbeppRVa680\nnb0xY8bw7LPPIolDDz2UwYMHbzM1ebx+kPsA3AfgWntOzeNFwjJXN+CHZbwmqVGXro9X3n4PEqrL\nv8+m2oz53K80StN0LE0rMSOutVd+rb1sOnvNmzdnwoQJdOvWjbFjx7LPPvtUntGO42wTeCDlVHUe\nJgQPs+NyXIqJBLmbm2L70ijmnG2cvHT8zOxtST8iaNq1K8elc4FmkjrFpb1GhKW9lN1j45LeHrFv\nqZp5cX4LJJ1kZk9HyaF2ZpZV4sfJj0WLFtGrVy8ASkpKOP300znqqKNo2LAhffr0oaSkhHr16nH/\n/fcX2FLHcQqNB1JOlSYumd2V4dQA4GFJswibrs8uZZyvJE2S9C7wv4TN5mVlJNDBzL4u6wVmtkbS\nKcDd8c3DYqAHYU/TUEmzCZvNzzGz73MEgOmUSyvRyY9sOntdunRh2rRpBbDIcZyqigdSTpUkU2V2\nMxsPjI+fl5FZ/HlA2nHbxOfT07qPT5y7NPG5W1q/LsDgTPYlbcowzhTgp+k2slGwODnecGB44rgo\n0zkzWwAclWFMx3EcpwB4HSnHyUIs4jkPKDazMYW2x3Ecx6l6eEbKcbJgZt+w8Y06x3Ecx9kMz0g5\njlOlWb16NZ07d6Z9+/a0adOG/v37bzh3991307p1a9q0aeNyLY7jFATPSDlbFUkrM+1/KhSxEvoF\nwBLC34c/mdmL5bi+GzAKWECo6fSkmd1Q8ZbWXOrWrcvYsWNp2LAha9eupUuXLhx99NEUFxczatQo\nZs6cSd26dVm8eHGhTXUcpwbiGSnHgcGxNtNJhDcBy/T3QlLqPyIT4/UHA7+VVGopg7TrnRxIomHD\nEHuvXbuWtWvXIomhQ4dyzTXXULduqEnatGnTQprpOE4Nxf8hdwpCVdSoM7N/SyoBdpZkwH2EOk8A\nV5jZpJjB2oug5fcx8LfE9askTQP2ljSToO/XjZCpusfM/hbnfRPwNdBa0oGE8gotgNrATWb2lKRf\nxrnUAaYAF8cyCQuBR4BfE8ofnFRdtfaSunfr1q2jY8eOfPjhh1xyySX85Cc/Yd68eUycOJFrr72W\nevXqcfvtt9OpU6cCWuw4Tk3EM1JOITkQuALYnxCYHCJpe4JGXR8za0+ovVQMXELUqCNIxjwiqV4c\npy1wPNAJuAX4zswOBN4kaNRBkEu5zMw6Av0I9Zw2IWrXrScs891JyFR1IkjRPJjouj/Qw8xOS7v+\nh4RyB3OA84Hl8fpOwAWS9oxdD4rz25dQyuBzM2sfSzW8Euc1nKDTdwAhmLo4caulZnYQIbDsl8vB\n1YXatWszY8YMPv30UyZPnsy7775LSUkJy5Yt46233mLgwIGcfPLJJDSnHcdxtgqekXIKSVXRqOsr\n6beE6uenxKxYD2D/RP/GcRyAF82sOHH9LyS9QwjCbjWzOZJuiPc+MfbZkZAxWxPnvSBh4yBJfwVe\nMrOJktoDC8xsXuzzCCGQHBKPn4s/pxECyM2oDqLF2URFi4qKuOeee2jQoAEtW7ZkwoQJAKxZs4ZR\no0bRpEmTza5xoVb3AbgPwH0ALlrsVC+qikbdYDO7Pa2tFvBTM1udbIyB1aq0vhPN7Ni0NhEyYK+m\nXd8teb2ZzYt7qn4F3CxpDGHzei5S88vqs6RocatWreyyM44rZciqy5IlS9huu+1o0qQJxcXFXHfd\ndVx99dW0b9+ezz//nG7dujFv3jxq1arFcccdhzJUiXehVvcBuA/AfQAV7wNf2nOqGhs06gAkNYqb\nslMadaRp1JVKzGotkHRSvF4x65OL0cBlqQNJ5RIKBl4FLpa0XcpmSTukd5LUnLAU+RgwkLDsNxco\nkrR37HYmMKGc9682fPHFF3Tv3p127drRqVMnDj/8cI499ljOO+885s+fT9u2bTn11FN55JFHMgZR\njuM4lYlnpJwqRRXSqLscuCdq+dUBXgd6l2MqDxKWKqdHceElZJC0AQ4ABkpaT9hQf7GZrZZ0LmEp\nMrXZ/L5y3Lta0a5dO955553N2rfffnsee+yxAljkOI6zEfnmTMep3rRq1crmzi1T8q7a4ssZ7gNw\nH4D7AMrmA0nTzOzgsoznS3uO4ziO4zh54oGU4ziO4zhOnngg5ThOTs477zyaNm1K27ZtN7Q9/fTT\ntGnThlq1ajF16tQCWuc4jlNYPJByNiDJJA1KHPeLlbwrYuzhiZpK+Y7RQtIoSR9I+kjSnbGAZ+r8\nCEmzJPWN91sgaaakeZIeldRiy2eSt+0DJOVVPFNSkaTTK9qmsnLOOefwyiuvbNLWtm1bnnvuObp2\n7VogqxzHcaoGHkg5Sb4Hjpe0c6ENSSKpTnzz7TngBTPbh1CMsyGhkjmSdgM6mVk7MxscL70yVkdv\nBbwDjE0GXtsQRUDBAqmuXbuy0047bdK233770apVqwJZ5DiOU3Xw8gdOkhJCEce+wLXJE5KGEypv\nPxOPV5pZw1hg8gbgG8Kr/CMJ1br7APWBnmb2URymh6RrgMYErbyXJNWmDJp0BImU1WY2DMDM1knq\nS6gP1Z9Q92n3WCF9Q/2n2NeAwZJ6AUcDoyQdEe2uC3wEnGtmK6OW3cjYrxg43cw+lLQL2bX39iBI\n3OwBDDGzu6KPrgXOBhYDnxAqkZNN9y/6eAVBT3A34Kro71uB/eLcHolzHUbQFKwFnGBmH2T8jZK/\n1l5S685xHMfJjGeknHTuAc6QtGM5rmlPqLG0H6F45L5m1plQSykZ1BQBnYFjgPuiplxZNenaEAOR\nFLHQ5sfA3sBvgI/MrIOZTcxi53SCUPDOwJ8JenkHAVOB3yf6LY8ad//DRlmWXNp7rYEj49z6S9pO\nUkfgVKADoWp5Uk03l+5fM6ALcCwhgAK4hlA9vUPMtvUG7oyV2g8GPs0yX8dxHKeS8YyUswlmtkLS\no4SClMWl9Y9MMbMvACR9RMiYQMhMdU/0G2lm64EPJM0nBCBHUDZNuoogVb3zpwTh4UmxoOf2BIHj\nFCMSP1PLhLm09/5pZt8D30taDOwK/AJ43sy+A5D0YvxZmu7fC9FH70naNcs83gSujXu+nsuUBYdQ\n3AAAC5tJREFUjaoIrb2kFtWXX37JqlWrNtOn+uabb5g2bRorV64s9/hbE9cXcx+A+wDcB+Bae87W\nYQghezMs0VZCzGBKqkUIPlKUpnWXIr36q1FGTTrgPeDEtD6NCctpHwJNS5kTwIHAmHjP18zstCz9\nLMPnXNp75dEMLE33LzlWxrLtZvaEpLcJmb2XJV1kZmPT+lSo1t7ChQvZYYcdNiti16RJEzp27MjB\nB5epbl3B8CKE7gNwH4D7AFxrz9kKmNkywj6h8xPNC4GO8fNvCDIr5eUkSbXiHqGWBE25MmnSEQKg\nBpLOiv1qA4OA4amsTzaitt7lhGWzV4C3gENSWnaSdoj6fSlOSfxMZarKq733OtBTUn1JjYBfQ966\nf98CjRL3bgnMj3uxRgHtSrl+izjttNP42c9+xty5c2nRogUPPfQQzz//PC1atODNN9/kmGOO4cgj\nj6xMExzHcaosnpFysjEIuDRx/ABhk/ZMQjCyKuNVufkYmEzYbN47asqVSZPOzCxuFr9X0nWE/wS8\nDPwpx/0Gxr4NCMFTdzNbAyyRdA4wQlJqWe3PwLz4+QdRY+97IJW1Kpf2nplNl/QUQc9vMUEvL0V5\ndf9mAeui74cTlgLPlLQW+BL4S45rt5gRI0ZkbO/Vq1dl3tZxHGebwLX2HCdBfGvvYDNbWmhbKgrX\n2vPlDHAfgPsA3AfgWnuO4ziO4zhVBl/ac5wEZlZUaBscx3GcbQfPSDmO4ziO4+SJB1KOU4PIJEDs\nOI7j5I8HUk65ieLGjyWO60haIumlPMdrIul3ieNu2caSNF5Szg2Akqp2dUg2iBh/J6lpoq1Uu5UQ\nfy6LL9LJJEDsOI7j5I8HUk4+rALaSqofjw8HPtuC8ZoAvyu1V/VjKfCHrXnDTALEjuM4Tv54IOXk\ny8uEytoQai1tKDYkaSdJL0iaJektSe1i+wBJD8dMyvxYJBOCptxekmZIGhjbGkp6RtL7kh5XQk8l\njnWepCGJ4wskDU7r0y3ea7NxJHWS9IakmZImS2okqZ6kYZJmS3pHUvfY95w4n9ckLZR0qaTfxz5v\nSdop9ttL0iuSpkmaKKl1KT58GDgldX3C7iJJ7yaO+ymII+dF8dp1+V7qOI7jlIK/tefky5PA9XEJ\nrh0hKPhFPHcD8I6Z9ZR0GPAoQbwXgr5ed0Kl7rmShhJEedumZFOiPMyBBKHiz4FJwCHAvxL3H0nQ\nm7vSzNYC5wIXZbBzs3EkTQaeAk4xsykKUjPFQB9C7c8DYhA0OlHxvG0cqx5BkuZqMzswBm9nEWR1\n7icUGv1A0k8IYsSH5fDhyui3PkD/HP3KjdK09sqim1edcX0x9wG4D8B9AK6151QRzGyWpCJCNurl\ntNNdgBNiv7GSfhiDFcgs8JuJyWb2KYCkGYTq5xsCKTNbKWkscKykfwPbmdnsMo6zHPjCzKbEsVbE\n812Au2Pb+5L+A6QCqXFm9i3wraTlwD9i+2yC6HJpYsTZuAuYIen2MvQtM+lae8nic9l086ozXoTQ\nfQDuA3AfQMX7wAMpZ0t4Ebgd6Ab8sIzXlFXgtyz9HiRIxLzPpgLL+dyvNEoTZi5NjDgjZvaNpCeA\nSxLNGwSiI/XKb67jOI6zNfA9Us6W8DBwQ4ZM0ESCnlxqmW5pKuuThU1EecuKmb0N/Ag4ncQerTIw\nF2gmqVO0sZGkOml27wvsEfuWxZZ8xIhT3EFYlkwFeYuApjGTVxc4tozjlEomAWLHcRwnfzwj5eRN\nXDK7K8OpAcDDUeD3O+DsUsb5StKkuMH6f4F/lsOMkUAHM/u6rBeY2RpJpwB3xzcPi4EehD1NQyXN\nJmSFzjGz79P2ueeivGLEKXuWSnoe6BuP10q6kSDw/Bkh41YhZBMgdhzHcfLDRYudbZq42X2wmY0p\ntC1VFRct9n0h4D4A9wG4D8BFix0H2FDEcx5Q7EGU4ziOUyh8ac/ZJjGzb9j4Rl2VRdK1wElpzU+b\n2S2FsMdxHMepWDyQcpxKJAZMHjQ5juNUU3xpz3Ecx3EcJ088kHIcx3Ecx8kTD6Qcx3Ecx3HyxMsf\nOE41R9K3lLGwaDVmZ2BpoY0oMO4D9wG4D6BsPvixme1SlsF8s7njVH/mlrUeSnVF0lT3gfvAfeA+\ngIr3gS/tOY7jOI7j5IkHUo7jOI7jOHnigZTjVH/uL7QBVQD3gfsA3AfgPoAK9oFvNnccx3Ecx8kT\nz0g5juM4juPkiQdSjuM4juM4eeKBlONUUyQdJWmupA8lXVNoe7YWkhZKmi1phqSpsW0nSa9J+iD+\n/EGh7axIJD0sabGkdxNtWecs6Y/xuZgr6cjCWF2xZPHBAEmfxWdhhqRfJc5VRx/8SNI4Se9JmiOp\nT2yvMc9CDh9U2rPge6QcpxoiqTYwDzgc+BSYApxmZu8V1LCtgKSFwMFmtjTRdhuwzMxujUHlD8zs\n6kLZWNFI6gqsBB41s7axLeOcJe0PjAA6A82B/wP2NbN1BTK/QsjigwHASjO7Pa1vdfVBM6CZmU2X\n1AiYBvQEzqGGPAs5fHAylfQseEbKcaonnYEPzWy+ma0BngSOK7BNheQ44JH4+RHCP6zVBjN7HViW\n1pxtzscBT5rZ92a2APiQ8Lxs02TxQTaqqw++MLPp8fO3wL+B3alBz0IOH2Rji33ggZTjVE92Bz5J\nHH9K7n9MqhMG/J+kaZIujG27mtkX8fOXwK6FMW2rkm3ONe3ZuEzSrLj0l1rSqvY+kFQEHAi8TQ19\nFtJ8AJX0LHgg5ThOdaOLmXUAjgYuiUs+G7Cwn6FG7WmoiXOODAVaAh2AL4BBhTVn6yCpIfAscIWZ\nrUieqynPQgYfVNqz4IGU41RPPgN+lDhuEduqPWb2Wfy5GHiekKZfFPdOpPZQLC6chVuNbHOuMc+G\nmS0ys3Vmth54gI1LNtXWB5K2IwQQj5vZc7G5Rj0LmXxQmc+CB1KOUz2ZAuwjaU9J2wOnAi8W2KZK\nR9IOcYMpknYAjgDeJcz97NjtbGBUYSzcqmSb84vAqZLqStoT2AeYXAD7Kp1U8BDpRXgWoJr6QJKA\nh4B/m9kdiVM15lnI5oPKfBbqbJnJjuNURcysRNKlwKtAbeBhM5tTYLO2BrsCz4d/S6kDPGFmr0ia\nAoyUdD7wH8IbPNUGSSOAbsDOkj4F+gO3kmHOZjZH0kjgPaAEuGRbfksrRRYfdJPUgbCUtRC4CKqv\nD4BDgDOB2ZJmxLY/UbOehWw+OK2yngUvf+A4juM4jpMnvrTnOI7jOI6TJx5IOY7jOI7j5IkHUo7j\nOI7jOHnigZTjOI7jOE6eeCDlOI7jOI6TJ17+wHEcxyk3ktYBsxNNPc1sYYHMcZyC4eUPHMdxnHIj\naaWZNdyK96tjZiVb636OU1Z8ac9xHMepcCQ1k/S6pBmS3pX0i9h+lKTpkmZKGhPbdpL0QhSUfUtS\nu9g+QNLfJU0C/i6ptqSBkqbEvhcVcIqOA/jSnuM4jpMf9ROVoxeYWa+086cDr5rZLZJqAw0k7ULQ\nOetqZgsk7RT73gC8Y2Y9JR0GPEoQlwXYnyBEXSzpQmC5mXWSVBeYJGm0mS2ozIk6Ti48kHIcx3Hy\nodjMOuQ4PwV4OArIvmBmMyR1A15PBT5mtiz27QKcENvGSvqhpMbx3ItmVhw/HwG0k3RiPN6RoI3m\ngZRTMDyQchzHcSocM3tdUlfgGGC4pDuAr/MYalXis4DLzOzVirDRcSoC3yPlOI7jVDiSfgwsMrMH\ngAeBg4C3gK6S9ox9Ukt7E4EzYls3YKmZrcgw7KvAxTHLhaR9Je1QqRNxnFLwjJTjOI5TGXQDrpS0\nFlgJnGVmS+I+p+ck1QIWA4cDAwjLgLOA74Czs4z5IFAETJckYAnQszIn4Til4eUPHMdxHMdx8sSX\n9hzHcRzHcfLEAynHcRzHcZw88UDKcRzHcRwnTzyQchzHcRzHyRMPpBzHcRzHcfLEAynHcRzHcZw8\n8UDKcRzHcRwnT/4fDYttIUhUupYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28451c75c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0]\ttrain-auc:0.809854\tval-auc:0.821619\n",
      "Multiple eval metrics have been passed: 'val-auc' will be used for early stopping.\n",
      "\n",
      "Will train until val-auc hasn't improved in 30 rounds.\n",
      "[1]\ttrain-auc:0.814364\tval-auc:0.825362\n",
      "[2]\ttrain-auc:0.845888\tval-auc:0.852807\n",
      "[3]\ttrain-auc:0.843779\tval-auc:0.852398\n",
      "[4]\ttrain-auc:0.847864\tval-auc:0.854376\n",
      "[5]\ttrain-auc:0.849654\tval-auc:0.856155\n",
      "[6]\ttrain-auc:0.849497\tval-auc:0.856105\n",
      "[7]\ttrain-auc:0.852149\tval-auc:0.858205\n",
      "[8]\ttrain-auc:0.856579\tval-auc:0.861535\n",
      "[9]\ttrain-auc:0.856804\tval-auc:0.861634\n",
      "[10]\ttrain-auc:0.855722\tval-auc:0.860985\n",
      "[11]\ttrain-auc:0.85551\tval-auc:0.860913\n",
      "[12]\ttrain-auc:0.856192\tval-auc:0.861299\n",
      "[13]\ttrain-auc:0.856859\tval-auc:0.862222\n",
      "[14]\ttrain-auc:0.857494\tval-auc:0.862671\n",
      "[15]\ttrain-auc:0.858189\tval-auc:0.863323\n",
      "[16]\ttrain-auc:0.858242\tval-auc:0.863378\n",
      "[17]\ttrain-auc:0.858159\tval-auc:0.863262\n",
      "[18]\ttrain-auc:0.857959\tval-auc:0.863229\n",
      "[19]\ttrain-auc:0.857936\tval-auc:0.863305\n",
      "[20]\ttrain-auc:0.857522\tval-auc:0.863175\n",
      "[21]\ttrain-auc:0.857698\tval-auc:0.863268\n",
      "[22]\ttrain-auc:0.858526\tval-auc:0.863679\n",
      "[23]\ttrain-auc:0.858501\tval-auc:0.863915\n",
      "[24]\ttrain-auc:0.858256\tval-auc:0.863769\n",
      "[25]\ttrain-auc:0.858886\tval-auc:0.864176\n",
      "[26]\ttrain-auc:0.858557\tval-auc:0.864136\n",
      "[27]\ttrain-auc:0.859118\tval-auc:0.864423\n",
      "[28]\ttrain-auc:0.859202\tval-auc:0.864459\n",
      "[29]\ttrain-auc:0.859399\tval-auc:0.86473\n",
      "[30]\ttrain-auc:0.859143\tval-auc:0.864484\n",
      "[31]\ttrain-auc:0.859219\tval-auc:0.864585\n",
      "[32]\ttrain-auc:0.859161\tval-auc:0.864405\n",
      "[33]\ttrain-auc:0.858884\tval-auc:0.864367\n",
      "[34]\ttrain-auc:0.858681\tval-auc:0.864226\n",
      "[35]\ttrain-auc:0.858842\tval-auc:0.864352\n",
      "[36]\ttrain-auc:0.858632\tval-auc:0.864164\n",
      "[37]\ttrain-auc:0.858726\tval-auc:0.864256\n",
      "[38]\ttrain-auc:0.858417\tval-auc:0.864103\n",
      "[39]\ttrain-auc:0.859195\tval-auc:0.864638\n",
      "[40]\ttrain-auc:0.858987\tval-auc:0.864521\n",
      "[41]\ttrain-auc:0.859202\tval-auc:0.864756\n",
      "[42]\ttrain-auc:0.85902\tval-auc:0.864685\n",
      "[43]\ttrain-auc:0.859708\tval-auc:0.86521\n",
      "[44]\ttrain-auc:0.85951\tval-auc:0.865082\n",
      "[45]\ttrain-auc:0.85938\tval-auc:0.864995\n",
      "[46]\ttrain-auc:0.859537\tval-auc:0.865116\n",
      "[47]\ttrain-auc:0.859941\tval-auc:0.865477\n",
      "[48]\ttrain-auc:0.859676\tval-auc:0.865318\n",
      "[49]\ttrain-auc:0.859511\tval-auc:0.865309\n",
      "[50]\ttrain-auc:0.859722\tval-auc:0.865389\n",
      "[51]\ttrain-auc:0.860141\tval-auc:0.865679\n",
      "[52]\ttrain-auc:0.860266\tval-auc:0.865823\n",
      "[53]\ttrain-auc:0.860263\tval-auc:0.865835\n",
      "[54]\ttrain-auc:0.860113\tval-auc:0.865718\n",
      "[55]\ttrain-auc:0.860586\tval-auc:0.866087\n",
      "[56]\ttrain-auc:0.860791\tval-auc:0.866221\n",
      "[57]\ttrain-auc:0.860646\tval-auc:0.866078\n",
      "[58]\ttrain-auc:0.860617\tval-auc:0.86613\n",
      "[59]\ttrain-auc:0.860743\tval-auc:0.866223\n",
      "[60]\ttrain-auc:0.86093\tval-auc:0.866339\n",
      "[61]\ttrain-auc:0.861175\tval-auc:0.866541\n",
      "[62]\ttrain-auc:0.861322\tval-auc:0.866612\n",
      "[63]\ttrain-auc:0.861376\tval-auc:0.866665\n",
      "[64]\ttrain-auc:0.861287\tval-auc:0.866627\n",
      "[65]\ttrain-auc:0.861307\tval-auc:0.86663\n",
      "[66]\ttrain-auc:0.86138\tval-auc:0.866658\n",
      "[67]\ttrain-auc:0.861456\tval-auc:0.866677\n",
      "[68]\ttrain-auc:0.861562\tval-auc:0.8667\n",
      "[69]\ttrain-auc:0.861569\tval-auc:0.866716\n",
      "[70]\ttrain-auc:0.861555\tval-auc:0.866819\n",
      "[71]\ttrain-auc:0.861592\tval-auc:0.866764\n",
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    {
     "name": "stdout",
     "output_type": "stream",
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      "[332]\ttrain-auc:0.87423\tval-auc:0.871071\n",
      "Stopping. Best iteration:\n",
      "[302]\ttrain-auc:0.873514\tval-auc:0.871083\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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Wit4jJJu/RcivIY51r6TZhJmJl4BH4rHngSclHU1INi+LM4FdCW/LASwxs8MljSK8nfdf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\nx8rqmKCyjYrPJAR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SrxP7LSQ9WZ57cpx0Up56s2fPZtasWbz00ku8/vrrjBgxgkMOOYQP\nPviAQw45hBEjRgCw22678fzzzzNnzhzGjBnD6aefXs134Dg1k5wCKTMbZ2bdzOxXcf9jMzu+rPMc\nJ09YE2sydQb6E5K/r/se4/0mbT81fhfCW3jnl3F+E6AkkDKzJWZ2wvfQ4zhZPfXGjx/PGWecAcAZ\nZ5zBs8+GdLz99tuPFi1aANC5c2fWrFnDd999Vz3iHacGk1OOlKR9gXuBZmbWRVI34Cgzu6lK1TnO\nVsbMlkZ7lemShhP+2BgB9AXqA/eY2f2x+07RzmUfYAoh+LkZaBgTxeea2Wlpl3gN6AYgqQAYD+wM\nbAdcbWbj4/X2jmNMAu4BJsSfvQaEn8X9gfXApWY2pbR7cq+9yifftPaN25k89T7//HOaN28OwA9+\n8AM+//zzLcZ46qmn6NmzJ/Xr1996wh0nT5CZld1JehW4ArjfzPaLbe/Gv7AdJ6+RVGxmBWltK4H2\nwNFAUzO7SVJ9YBpwIrAXoQZUJ+Dfcft+M3syfbzUvqS6wOPAg2b2UixtsEN8kWM3Qg2pdnHsCamf\nL0mt2RRIXQZ0NrOzYwHPicC+ZvZtmv6k117htXeMqsQnVjU0awifr6luFbmRb1qb7tJ4s7aUp95F\nF13EhRdeyIQJm1apjzzySJ5//vmS/YULF3L11Vdz66230rJlyyrVWlxcXDJrVtPJF635ohNqltZ+\n/frNNLP9c+mb61t7O5jZmwl/Lwh/DTtObWcA0E1SammtMSHYWQu8aWYfA0h6DOgDZMplSs1QtQTe\nJ8wyAQi4WdLBwMZ4vFkZevoAdwGY2TxJ/yb48G32Fq177VUt+aY1k39ZylOvZcuWtG/fnubNm/PZ\nZ5/RokWLkv6ffPIJv/jFL3jiiSf40Y9+VG1aayL5ojVfdEJ+aU2Sa7L5ckl7AwYQf6l8VmWqHKca\niS9TbACWEoKdCxPedm3MbGLsmj6dm216d42Z9SDMNIlNOVKnAbsDhfH450CDSrwVxykhm6feUUcd\nxZgxYwAYM2ZMiRnxypUrOeKIIxgxYsRWCaIcJ1/JNZA6H7gf6CDpU+AS3JbCqYVI2h24D7jbwrr3\n34FfSdouHt9XUqPYvbekNvENv5OBf8b2dan+ScxsNcHi5bK4rNcYWGpm6yT1IwRaAN8AO2aROJUQ\ngKVyF/ckWMs4Tqlk89QbOnQokyZNol27drz88ssMHToUgLvvvpsPP/yQG264gR49etCjRw+WLl1a\nzXfhODWPMpf24i+J/c3s0PgLpI6ZfVP10hxnq5FaetuOsGT9MJCq3P9noDXwlsLa9jLgmHhsOnA3\nm5LNn4ntDwDvSHorPdnczN6OxWxPBR4Fnpc0B5hB8NTDzFZImibpXeBvhGTzFH8C7o3nrAfOjDYz\njlMq2Tz1dt11V1555ZUt2q+++mquvvrqrSHNcfKaMgMpM9so6UrgCTNbtRU0Oc5WxczqlnJsI6Gc\nQXpJgyLg4CznXAVcldgvSDt+ZGL3wCxj/E9aU5fY/i1wVja9juM4ztYl16W9lyVdLmkPSbukPlWq\nzHEcx3Ecp4aT61t7Kbf5ZCFBA9pWrhzHcRzHcZz8IdfK5m0yfDyIchzH2cosXryYfv360alTJzp3\n7sydd94JwMknn1ySFN66dWt69OhRcs4tt9zCPvvsQ/v27fn73/9eXdIdp1aSa2Xzn2dqN7O/VK4c\np7aTqfhldRKrl59HSCKvB/zGzJ4rx/l9CdXJFxIqnz9uZtdXvlLHCdSrV4/bbruNnj178s0331BY\nWEj//v0ZO3ZsSZ/LLruMxo1DEc5Fixbx+OOPM3fuXJYsWcKhhx7KggULqFs3a2qg4zjlINccqV6J\nz4+B4cBRVaTJcbY2t8c6TicCD5VlWJwiljAAmBrP3x8YLKlnOc93nJxp3rw5PXuGb7Edd9yRjh07\n8umnn5YcNzOeeOIJTj31VACmTZvGKaecQv369WnTpg377LMPb775ZrVod5zaSE7/kZvZhcn96F7/\neJUocrYJ4kzOcGA54Y20mcBgMzNJvYA7gUbAd8AhwDoyeMxJOpNQjqARoeL4SGB74PR47uFm9kUs\nKHsPoQDmauA8M5uX1GRm70taD+wmyQj1pPaMhy8xs2lxBmtvQn7gfwj11VLnr5I0E9hH0mwyePTF\n+74R+JJQl20/4AmgFVAXuNHMxko6JN5LPUKZhV+Z2XeSFgFjgCMJ5RpOTL+PdNxrr/KpLq2LRhyx\n+f6iRbz99tsccMABJW1Tp06lWbNmtGvXDoDly5fzk5/8pOR4q1atNgu8HMf5flT0L+JVQJvKFOJs\nk+wHdAaWEDzsfiTpTWAscLKZTZe0E7AGuBgwM+ua8piLBSkhBGL7EaqCfwhcZWb7Sbod+DlwB6G2\n0xAz+0DSAYR6TD9NiontGwnLfI8SZqr+KWlPQmHOjrFrJ6CPma2JgVHq/F2BHxICpXOAr8ysV8qj\nT1KqInpPoIuZLZR0PLDEzI6IYzSOxsSjgUPMbIGkvwC/ivcBsNzMekr6NXA5cG76g03z2uParjXf\n0alZwxCg5APVpbWoqKhke82aNVx88cWce+65vPXWWyXtt99+O7179y7pu27dOt5///2S/c8++4y5\nc+ey2267bUXluVFcXLzZPdZk8kVrvuiE/NK6GWZW5gd4HngufiYAHwO/y+Vc//gn+QGK4799gUmJ\n9nuBwUBXYFqG854BfprYnwp0A84ERiXa/wO0jNtnE4KPAkIwNivxeT/2GQ58GtumAj+O7UvT+n8a\nxxkOXJe4Xl/gK+BtwqzakNj+JLAgcf5Cgm9fX2BK4vx9gUXA7xLX7g78I9HnEODpuL0ocX8HAC+X\n9cz33XdfywemTJlS3RJyprq1rl271gYMGGC33XbbZu3r1q2zpk2b2uLFi0vazj33XLv55ptL9gcM\nGGD/+te/tprW8lDdz7U85IvWfNFpVrO0AjMsx99ruc5IjUxsrwf+bWaf5Hiu42QjWZF7AxWfIU2O\nszGxvzGOWQdYaSGPKRO3m9nItLY6wA8tFMAsIRp3pxemnWpmg9LaUh59m70iFWewSs63MOPUEzgc\nuEnSK4Tk9dJI3d/3eWZOnmJmnHPOOXTs2JFLL710s2Mvv/wyHTp0oFWrViVtBx10EHfccQeXXnop\nS5Ys4YMPPqB3795bW7bj1FpyTTY/3MxejZ9pZvaJpN9VqTJnW2U+0DzmSSFpx5iUXWGPOTP7Glgo\n6cR4viR1L+O0iUBJbqCkbEFYNkrz6CtBUgtgtZk9AvyesOw3H2gtaZ/Y7XTg1XJe36mlTJs2jYcf\nfpjJkyeXlDt48cUXAXj88cdLksxTtGnThpNOOolOnTrxs5/9jHvuucff2HOcSiTXv2b7k7C8iByW\noc1xvhdmtlbSycBdkhoSluQOJYvHXJwhyoXT4vlXE5K0Hwdml9L/IuCe6ItXD/gH5TPqLs2jL0lX\n4PeSNhIS6n9lZt9KOgsYF4PI6YTEd8ehT58+qSXfLRg9enTG9mHDhjFs2LAqVOU42y6lBlKSfgX8\nGmgbf6Gk2JGQHOw45cJiDSkzKyL41aXaL0hsTyckbaezhcecmY0mJGan9ltnOmZmC4GfZTh/eBad\ny9lU0T9r//T7SLSX5tFXlOj3d8LsVfr5rxAS6NPbWye2ZxByrhzHcZxqoqwZqb8S3OdvAYYm2r8x\nsy+qTJXjOI7jOE4eUGogZWZfEd5IOhVAUlPCK+YFkgrM7D9VL9FxHMdxHKdmkmsF5yMlfUB4hftV\nwivYf6tCXY7jOE4a2Xz2AO666y46dOhA586dufLKK4FQQ+qWW26ha9eudOzYkVtuuaW6pDtOrSXX\nZPObCDkrL1sodNiPUPPHicRK2H8ws8vi/uVAQbYcnHKOPRqYYGZPfo8xNgBzCF/zhcDpZraygmMt\nAvY3s+WJcVM8bmYjspx3DLDAzN4rY/xc+w0n1KVKL11QpUjqA/wB2Ck2/cHMHsjS90zCs7ogrf1F\n4H8q+jVwtk2y+ex9/vnnjB8/ntmzZ1O/fn2WLl0KwLhx41i3bh1z5sxh9erVdOrUiVNPPZXWrVtX\n7404Ti0i1/IH68xsBVBHUh0zm0Kw6nA28R1wnKQaVS444ee2xsx6mFkX4Avg/Eq6RGrc1CdjEBU5\nhlAVvCxy7bfVkfQDQu7gEDPrAPQBfinpiAx9s/6hYmaHexDllJdsPnv33nsvQ4cOpX79+gA0bdoU\nCHXPvv32W9avX8+aNWvYfvvt2WmnnbKO7zhO+cl1RmqlpAJCLZ9HJS1ly6KE2zrrCTYk/wts9p5x\n+oySpGIzK4jFGa8HVhJeg3+CMLtzMdAQOMbMPorDHCppKGEW5FIzmyCpLjn4uRGqZyd5jVAVPKXv\nCuCkOMYzZnZdbH8W2IOQF3dntlmXTEgaQTC2Xk+oyfR03P9JLEFwPMGi5RcEb7wPCfWSemToB2X4\n5KVd+1JCVXOAP5vZHaXdj6RigrffIEK5haPN7PNYd+o6QuHLr8zsYEIAOtrM3oLwdp+kKwkVz1+I\nX+tvCW/cTQOSb7smNS4i/DFSQFgm/ydwEKGC+tEW7Gcy+gNm0ZUV99qrfKpDa2k+e1dccQVTp05l\n2LBhNGjQgJEjR9KrVy9OOOEERo0aRfPmzVm9ejW33347u+yyy1bV7Ti1nVwDqaMJv2AuIdTjaQzc\nUFWi8ph7gHck3VqOc7oTPNy+IFjv/NnMeku6mFAQ8pLYrzXQm2CYOyUWa/w5Ofi5JS8Wg69DgAfj\n/gCC2W9vQjXu5yQdbGb/AM62YPjbEJgu6ak4M5mkoaRZif1bgJeBY4EOZmaSmpjZSknPsXlAudLM\nRsXtm4BzzOyuDP1eoQyfvMT9FRLKJBwQ7+cNSa+a2dul3E8j4HUzGxa/ducRlrOvBQaa2acKRt0Q\nvAHHpF12RmxP0Qo4yMw2xKW9smgHnGpm50l6ghA8PkJ2f8BMutKfg3vtVSHVobU0n72vvvqKOXPm\nMGLECObNm8dRRx3FX//6V9599102btzIY489xjfffMPFF19MQUEBLVq02KracyWfvNbyRWu+6IT8\n0pokp0DKgqv9XkA7MxsjaQeCU72TwMy+jgazFxECz1yYbmafAUj6iDB7A2Fmql+i3xOxNtEHkj4m\nzDQNALpJOiH2aUz4pbwWeDMtiEoFPC2B94FJsX1A/Lwd9wviGP8ALpJ0bGzfI7anB1Jr0q1X4pLW\nt8CDkiYQ/Bkz0SUGUE3idbeopxRnQg8iFKdMNdfPMh6EpbZnzGxVPP9p4Mfx/rLdz9qExpmEArQQ\nZpRGx+Dm6VKumc44M9tQjv4LzSwVjM4kVDUv7b7L1BVn2x4AaN++vV142tHlkFM9FBUVcVLfvtUt\nIyeqU+u6desYNGgQQ4YMKbGIad++PRdeeCH9+vWjX79+jBw5ki5duvDkk09y0EEHceihhwLw/PPP\nU69ePfrW0OdcVFRUY7Wlky9a80Un5JfWJLm+tXcewYT1/tjUEni2qkTlOXcA5xBmOVKsJz5rSXUI\nS1kpyvKJS5FeytjY5OeWyk9qY2apQCx96TUV8OwVz0vlSAm4JTHGPmb2YFwePBQ40My6EwKRBmXf\nPpjZesIM15OE5bKXsnQdDVxgZl0JS5yZxi/xyUt8OuaiI0kZ97PONpWKLvGvM7MhwNWEoGumpF2B\n94DCtOELgbmJ/fIue2fyHMx631l0OdsAlsVn75hjjmHKlCkALFiwgLVr17Lbbrux55578vbb4W+k\nVatW8frrr9OhQ4dq0e44tZVck83PB34EfA1gZh8ATatKVD4TC5U+QQimUixi0y/fowgWJeXlREl1\nYt5MW4IfW05+bmn6VhNmzC6LM0d/B86OMyBIahnrhTUGvjSz1ZI6kLnSeEbiWI3N7EVCzljK1+4b\nQlX8FDsCn0X9pyXaS/pZ+X3ypgLHSNohPotjY1u570fS3mb2hpldS7B42YOwfHumovdeDGJ+B5Rn\nObdMSrvvLLqcbYBsPntnn302H3/8MV26dOGUU05hzJgxSOL8889nzZo1dO7cmV69enHWWWfRrVu3\nsi/kOE7O5Joj9Z0FDzSgZOkms9mTA3AbkHzdfRQwXtJswuxMRRL1/wO8SUg2H2LBjy1XP7fNMLO3\nFSx/TjWzhyV1BF6LX99iQmmLl4Ahkt4nBG2vZxkuPUfqJULi9nhJDQgzXqk/nR8HRkm6CDgBuAZ4\nI+p+g01BVnq/0nzyrpaUyiPDzFrFhO83Y9Of4/2+l+P9JPm9pHbxHl4BZsecr8FR347x2B1m9nwp\n45ypUNIhRa5Babb73kJXjuM5eU5pPnuPPPLIFm0FBQUMHz48L5dLHCdfULYfys06hQTclYTk5gsJ\n/nvvmZm7YDpODad9+/Y2f/786pZRJvmUH+FaqwbXWvnki06oWVolzTSznMo85bq0N5QwazAH+CXw\nIiFHw3Ecx3EcZ5ul1KU9SXua2X/i22Kj4sdxHMdxHMeh7BmpkjfzJD1VxVocx3GcUiiv1x7ARx99\nxIEHHkjnzp3p2rUr3377bXVId5xaS1nJ5kpst61KIY7jOE7plNdrb/369dx88808/fTTdO/enRUr\nVrDddhV5adhxnGyUFUhZlm3HcWoIkuqWswCok6c0b96c5s2bA5t77Y0aNSqj197EiRNp27Yt3buH\niiG77uolxxynsikrkOou6WvCzFTDuE3cNzNz90tnmyKTX5+kc4CrCG+2ziaUC7lA0u7AfcCe8fRL\nzGxalnF/QigbAeGPloMJpSjuIlRaX0yowP6QmT0ZvfrGxmO3EkojZMS99iqfra013WcPcvPaW7Bg\nAZIYOHAgy5Yt45RTTtls2c9xnO9PqYGUmbkNjONsTrpf3wuEelg9CYVEJ7OprtOdwO1m9k9JexKK\nn2aryn45cL6ZTYsFTb8lFBNtD3QCmhGqqj+UOGeFmfXMNJh77VUtW1truv9Yrl578+fPZ/bs2Tzw\nwAPUr1+fyy67jLp161JYmF6cv2aQT15r+aI1X3RCfmndDDPzj3/8k+MHGE4IlGYDXxFKg4xJHL8I\nuDtuLwVmJT6fAgVZxh1KKEp6EdAqtt1BCNxSfZ4GTojbi4C9ctG87777Wj4wZcqU6paQM9Wpde3a\ntTZgwAC77bbbStoGDhxokydPLtlv27atLV261B577DEbMGBASfsNN9xgt95661bVWx78e6DyyRed\nZjVLKzDDcvy9kGsdKcfZ5sni1zevlFPqAD+0TV55Lc2sOFNHMxsBnAs0BKZFG5uyqEiFfCePMSuf\n197AgQNZuHAhq1evZv369bz66qt06tSpuuQ7Tq3EAynHyZ1Mfn2NgJ9I2jlaJx2f6D+R4AQAQMqf\nL8WwyD8AACAASURBVBPRP2+Omf0OmA50AP4BnCyprqTmQL/KvyUnnyiv197OO+/MiSeeSK9evejR\nowc9e/bkiCO2zLdyHKfi5Oq15zhOZv/BT4GbCd5+XxBmqL6K/S8C7om+hvUIgdGQLGNfIqkfsBGY\nC/yNkFz+U0Ju1H+A16rgnpw8orxeewD9+/fnt7/9bVXKcpxtGg+kHCdHzOw74LD0dkkz/n97Zx4m\nVXXm/88XWlwAJUTUFlTEQXZocYkaxFZBTWREjMZtCKgxmlEHdxnNuE0ctyCogybihkaJorJo/KFE\nQCMKCAg0IogKDG4giguLyvL+/jinmktRvVJNVzXv53nqqXvPPffc77lVTb2cc+77tfD0XgEwipjI\n1sxWAGdUsu1LyzhUan4dzZhT9VtWWrjjOI5TY/jUnuNsPTdJmgXMBRaRcARwHMdx6jY+IuU4W4mZ\nXVXZupLOBQakFU82s4srcZ3+VZTmOI7j1DA+IuU42xAzezTxFF/qVWEQ5dRtyvPQAxg0aBCSWLFi\nBQDTpk0rXWzepUsXRo0aVRuyHcdhOwukJJmkQYn9qyTdlKW2H5N02la20ULSGEkLJX0o6R5JDRLH\nR0iaI+lyBf4Q674vaaKkDlvfk4y6DpT0UrzWTEnPSNpzK9q7SdJVcfsWST3i9mWSdknUWyxp97Rz\nT5Y0sLrXLkfT7pLWSSprMXhF56+K7y0lzc2uOqeuk/LQmzdvHlOmTGHo0KHMmzcPCEHWK6+8wr77\n7ltav2PHjkyfPp1Zs2Yxbtw4LrzwQtavz49kpo5T19iuAingB+DU9B/n2kZSgSQREi6ONrPWwIFA\nI+DWWGcv4FAz62xmg4GLgSOBLmZ2IHAbMFbSTlnWthPwd+ABM2ttIZP2/UCz9D5Up30zu8HM/hF3\nLwN2qaD+2JhzKducTngK76waaNtxyqWwsJCuXUOS+qSHHsDll1/OnXfeSfgnIrDLLrtQUBD+5L7/\n/vvNjjmOs23Z3tZIrQceBC4Hrk8eiE9EvWhmz8b9VWbWKCZhvJngo9YJeAYoIaxz2Rk4xcw+jM30\niKMluwJXmNmLkuoDtwPFwI7AUDP7S2z3v4GVhJxBvwe+N7NHAcxsg6TLgUWSbiTkJGoeFzVfSvB2\nO9rM1sT6r0h6EzgHeDiOkAwDjgc+B840sy8kHQAMJQRCa4ALzGx+7P+3wCHAXsA18V6cDbxlZi+k\n7pWZTYr3qD9wKiHgq0/Ip3Q18OvY11FmdmOsez3Qj5DteykwI3nfgb3ja6KkFWaWMWdSvOYhFrzs\nytJMJh2SGsbPr0XU+99m9nRs+izgSuApSS3M7OPU94Bg9dILWAv0NrNlkvYHnop9H5NJa5ruIoLv\n3i7Ah4SM5SslXUCwcmkAfAD0jXmqMvYt5pN6mvAdKwB+b2b/LO/a7rWXfbKpNd1HL+mhN2bMGJo3\nb15qOpxk6tSpnHfeeSxZsoQnnniiNLByHGfbsj3+5Q0F5ki6swrndCF4pH0FfAQ8ZGaHSRpACGou\ni/VaAocBBxACgn8BfgN8Y2aHStqRkLX6lVi/K9DRzBZJ+g9icJHCzL6V9H/AvwAnEwK9Ikm7Ag3N\n7KM0ndOB1PReQ0KK+8sl3QDcSHiU/kHgIjNbKOlnhNGlY+M5hUA3QmA3FngW6JiuK42uQGcL/nPH\nA63jPRBhhKw7IQP3mUAR4Ts3M0Nf75V0BXBMTBtQWbbQXI6OZsCnZnYSgKTd4vs+QKGZTZP0DCFl\nQWoKuCEwxcyuj9+ZC4A/EoKrB8zscUmVWeP0OHCpmb0m6RbC53EZ8LyZDYs6/gicTzAqztg3QmD7\nspndGoP0jCN4cq+9GiWbWpPeYkkPvTfffJOBAwdy1113MWnSJL7//nsmT57MbrvtVlp/6NChLFmy\nhOuuu46GDRvSoEGDLdrPJ/8y15p98kUn5JfWJNtdIBWDk8cJyRLXVvK0t83sMwBJHxJGhyCMTCVH\nTp4xs43AQkkfEX4Ajwc6J9ZP7Ub4kf8RmGZmi7aqQ2WzkTByAfBX4HkFM9wjgZGJqYAdE+eMjvrn\nVWEN1Hgz+ypuHx9f78T9RoS+NiaMCq0BkDS2Gv0pi0yay9LxT2CQpDsIQWlqJOcMwkgVwN8IxsCp\nQOpHwogZhOCvZ9z+OZuymD8B3FGWwBiwNTGz12LRcGBk3O4YA6gmUefLFfTtbeARSTvE47MyXdPM\nHiQEzezb6l9sUEnu/6lf2Wk9+aATsqt18TnFAKxbt45evXpx0UUXccUVV1BSUsKXX37JJZeEVGIr\nVqzg0ksvZdq0aey1116btTF8+HCaNm3KIYccskX7kyZNori4OCtaaxrXmn3yRSfkl9Yk+fGvVvYZ\nQhgVeTRRtp64ZkxSPcJUS4ofEtsbE/sb2fwepqccNsKIyKVmlvyBTPm2Jb3S5gGnpdXZFdiXMOWz\nR2mjIRhcLalV2qjUwcBrZMZi/742s7KsSpL9TEVa7wJHl1GftD4IuM3M/pLWj8uoOTJpzqgjaukK\n/BL4o6RXzewWwrTeXpLOidX2ltTazBYC62xTKukNlP95V4fHCNPDs+O0ZXHi2BZ9M7PX4+jaScBj\nku42s8fLu8DOO9Rnwe25bwsyadKk0qAi18m21kweep06dWL58uWldVq2bMn06dPZfffdWbRoEfvs\nsw8FBQUsWbKE+fPn07Jly6zpcRyn8mxvi80BiCMozxCmUVIsJgQiEKbRdqhG06dLqhfXIbUi2Ii8\nDPw+jiCknoBrmOHcV4FdJP0m1qtPGBV5LDWSk8ZdwL2Sdo71exCmgZ6Kx+uxKTA7G3jDzL4lrLk6\nPZ4jSVsuvticp4AjJZX+EkvqLqljhrovA+fFkS8kNZe0B8Ea5RRJO0tqDPxrGdf6jjB6tbVk1CFp\nb2CNmf2VcP+6SjoQaBQNhVvGjOG3UfGi88mE6UoI69LKxMy+AVZKOioW9WVTwNsY+Cx+P8ptJ/Zl\nP2BZnA58iDC16uQ5ZXnolcUbb7xBly5dKCoqok+fPtx///3svntOPUPjONsN2+uIFIQg5ZLE/jBg\njKTZBE+11RnPKp//I3iu7UpYh/S9pIcIa6dmxifzvgBOST/RzExSH+B+Sf9FCIReAq4r41r3AT8B\nSiRtICwo721mqenK1cBhkv5AWOCdsio5B3gglu9AmMqaXVaHzGytpF7AEElDgHXAHLZMKpla8N4O\neCtOHa4C/s3MZkp6Ol5nOWF6KhMPAuMkfZpYbD5H0sa4/Uy8drmUpYOw1uyu2N46wgL/swi2Lkme\nI0yL3lLOZQYQFqZfy5aLzdtI+jixfzlhof2fFdI7fAScG4/9FzCV8L2YSsWBZDFwtaR1sV+/qaC+\nkweU56GXYvHixaXbffv2pW/fvjWsynGcyqCK/nid/ETxqcPa1uHUPm3atLEFCxbUtowKyaf1Ea61\nZnCt2SdfdEJuaZU0w8y2XHSYge1yas9xHMdxHCcbeCBVR/HRKMdxHMepeTyQchzHyQJl+eV99dVX\n9OzZk9atW9OzZ09WrlwJwI8//si5555Lp06d6NKlS17mz3EcxwOpnEV1xxdwtaRZkuZJWhu3Z0k6\nTQmfvZpA0rEK3oBzJQ1XtLGJTyveK+mDqLFr4pwNUd+7kmZLujKmw8imrlVVqFss6chsXt+pGcry\ny7v99ts57rjjWLhwIccddxy33x4cjoYNGwZASUkJ48eP58orr2Tjxo3lXcJxnBzEA6ncpa74AjaM\neat+CXxoZkXx9Wyaz162ddYjJL4808w6AksIT84B/IKQoLM1Ifv3A4lT10Z9HQjJN39ByEJeWxQT\nkqg6OU5ZfnljxoyhX7/w1evXrx+jR48GYN68eRx7bDAV2GOPPWjSpAnTp0+vHfGO41Sb7Tn9Qa5T\nZ3wBy/KCS/ZD0mJgBCFwWU8IcG4jpiwwsz/HcyrloQdMAH40s/fj5cYD/wk8DPQGHo+JNqdIaiKp\nMJW9PoWZLVewWnk7jgbuR8hinsoDdomZvamQKf95MxsdNT4Z9XxASPragPCfll/FJJ+Z7sW/An+I\ndb8kpKnYGbgI2CDp3wh2RPMJnn37xlMvM7PJmdpM4V572Sdda3l+ecuWLaOwsBCAvfbai2XLlgHQ\npUsXxo4dy1lnncXSpUuZMWMGS5cu5bDDDtt2HXEcZ6vxQCq3yXtfwCr29/+il+BgQsbvnwM7AXMJ\nOZiq4qH3LVAg6RAzm05ITrpPvE5zgnFyio9j2WaBVOzXRzHA3IOQA6tnzA/WmhD4HUIIzi4HRsdr\nH0kY/RoM3GNmT8Zpz/rl9P0N4PCYT+y3BJPiKyX9GVhlZn+KfXsKGGxmb0jal5B8tF16Y3KvvRol\nXWtZfnkzZ85k/fr1mx3fsGEDkyZN4oADDmD8+PG0bduWPffck7Zt2/Lee+9lfa1UPvmXudbsky86\nIb+0JvFAKofZjnwBU6Q8+EoI2ca/A76T9IOkJlTRQ0/SmcDgGBS+QrB42Rp2AP5XUlFs60CAaER8\nv6RmBP+958xsvaS3gOsltSCMWGUcjYq0AJ6WVEgYlSrrXvcA2muTV+KukhqZ2Wbrrtxrr2ZJ11qW\nXx5A8+bNadOmDYWFhXz22WfsvffepblyjjvuuNI2jjzySE499VTat2+fVa25lJunIlxr9skXnZBf\nWpPkx79a2zd57QtYRZJa0/tRQBU99MzsLeCoePx4YuADfMKm0SkIQcwnmQRJakUImpYT1kotI4z6\n1QO+T1R9nJA9/Uxi1nIze0rSVIIv3kuSLjSzCWX0/T7gbjMbG+/3TWXUq0cYufq+jONb4F572SeT\n1kx+eQAnn3wyw4cPZ+DAgQwfPpzevXsDsGbNGsyMhg0bMn78eAoKCrIeRDmOU/P4YvMcp474AmaL\nSnvoxeN7xPcdgWsJa4sgjHz9Jj69dzhhOnOLab04wvRn4H/jeqrdgM/iSF5fNp+qe4w4bWpm8+L5\nrYCPzOxego1M53L6thubgrl+ifJ0/8FXCFO0KY1VnT51aoiy/PIGDhzI+PHjad26Nf/4xz8YOHAg\nAMuXL6dr1660a9eOO+64gyeeeKKWe+A4TnXwEan8IN99AbNCFT30IHjS9Yr6HkiMBr1EGL36AFjD\nJt87gJ3jIvkdCCN/TwB3x2P3A8/FAHKz+25myyS9B4xOtPVroK+CL97nwP/E8l20uRff3YQRqJGS\nVhIWyu8fj70APCupNyGA+g9gqKQ5hL/f1wkL0p1apjy/vFdffXWLspYtW5IP1j2O45SPe+05ThZQ\nMCMuAbqa2Te1rSeJe+1lH9daM7jW7JMvOiG3tMq99hxn26GQVPQ94L5cC6Icx3GcmsWn9hxnK4lJ\nRferbR2O4zjOtsdHpBzHcSrBeeedxx577EHHjh1Ly2bNmsXhhx9OUVERhxxyCNOmTQNCGoR+/frR\nqVMn2rVrx2233VZbsh3HqWFqLJCSe8VVRctOkqYpeLu9K+nmxLGmksZHneMl/aSMNm6S9ElC3y9j\neQNJj0oqie0XJ85ZHMtLYv/+KGmnre1Pmq7UNeZIekXBPqaqbZwiqX1i/zFJi2J/3pf0eMzVVF2N\n/SVtlNQ5UTZXUssKzrsusT1Y0mWJ/Zfj4v3U/iBJV6S34eQP/fv3Z9y4cZuVXXPNNdx4443MmjWL\nW265hWuuuQaAkSNH8sMPP1BSUsKMGTP4y1/+wuLFi2tBteM4NU1Njki5V1zl+QE41sy6AEXAifGx\nfICBwKtR56txvywGJ/S9FMsuADCzTgTvuEHa3IT3mHjsMEIahC1yNGWBY8ysMzCd6j3ZdwqQnmDn\n6ni/2hASdE5IBsLV4GPSrHgqQbIvk4meePH+7g50SBw/EnizMo0qmis7uUX37t1p2rTpZmWS+Pbb\nbwH45ptv2HvvvUvLV69ezfr161m7di0NGjRg11133eaaHcepeWryH2z3iqukV1zMUZTKTL1DfKUe\np+wd+wPBhHcSISdSZWlPeJw+5R33NcHWZFqykpmtknQRsFRSU0I28zHAT6KeP5jZGEm3AF+Z2ZDY\nn1sJySqfAZ4mfB4FwO8z3LfXCY/vI+kB4FDC5/qsmd0Yy28n5MZaT/gcno/7R0v6AyFzeFK3EbKX\n9yHc+zGp71Ns7zSgl5n116a8UJl86l4EuktqY2abPeIm6SxC0CTg72Z2bdSZSpXwLnA1wRIGQgA1\nFyiMI4hrCDYuqbQSd0atBvzRzJ5O/45KOog0/8BY72BCuoRGwAqgf6YcWEnca2/rSPfRSzJkyBBO\nOOEErrrqKjZu3Mibb4ZY+bTTTmPMmDEUFhayZs0aBg8evEUQ5jhO3aCm/+frXnGV8Iozs9djEDgj\nXn+omU2Nbe6Z+KH8HNiznOtfqpDjaDpwpZmtBGYDJ0saQcjmfXB8n5Z+crwHi6LGGUCfWLY7wdx3\nLPAIIbgZEkdezoz96Q+8bGa3xr7skkFfL0JgDHC9mX0V674ap9U+AfoAbWOuqiZm9nW8bjLwztT3\nmYQgeUw59+ceyvap20gIcK4jkRBTIdnnHfG+rQRekXSKmQ2UdEnyOyJpfWz3SOAtgn/fEcA3QImZ\n/SjpV4RRxy6EUau3Jb0em0h+R39Fmn+gQqLU+4DeZvaFpDMIo6jnpXdU7rWXNZLeX59//jmrV68u\n9QS79957Of/88zn66KOZOHEip556KoMGDaKkpIQVK1YwYsQIvvvuOwYMGECjRo1KR6y2JfnkX+Za\ns0++6IT80pqkRgMp94qrtFfc62a2ASiK9UZJ6mhmc5ONx+CirMRfDxBGNCy+DyL8wD5CCBamA0sI\n00vlec4p8f4/CqbAGwlBwZ5mtljSl3HEZE/gHTP7UtLbwCPxx360mc1KtDlR0gZgDvCHWPbr+GNf\nABQSRs7mEWxXHpb0ImGUqLJkjK7SyOhTlzj+FMEbb/9E2aHAJDP7AkDSk0B3Nk+8meJNQhB1JGHU\nqHnc/oYw9QfQDRgRP+9lkl6L1/iWzb+jJaT5B0rqCHQExsc+1CeD0TK41142SVrBLF68mIYNG9Ko\nUSOKi4vp3bs3zz33HJI4+uijGTx4MMXFxYwcOZJ+/frRo0dYQvnCCy9QUFBQKzlycik3T0W41uyT\nLzohv7Qm2Rb/arlXXCW84lLEEZiJwImEkaxlkgrN7DMFQ9vlUe+jwEGEUYtfmtmyRF+GEYMQM1tP\nmF5NHXsTeD/TtSU1Joz0vQ+cAzQDDjazdXHqMrUQ/SHCCNRehECNOKrWneAr95iku83s8Vj/GDNb\nkbjO/sBVhHVoK+MU6U4WjH4PA44jfD6XAMeWda/SOIiwhgw2/24kF89n9KlLBVbx+oOo2tRpktQ6\nqU6Ez24pcCUhSHq0nPNSJDOlv680/0BgFPCumR1RFVHutVdz7L333rz22msUFxczYcIEWrduDcC+\n++7LhAkT6Nu3L6tXr2bKlClcdtllFbTmOE4+UuPpD9wrbjPK8oprFkeikLQzYVH4/HjOWDZNNfUj\nTl2Z2blxUXnq6bzCxHX6EH7IkbRL6h5I6gmsT3nBJYma7ieMJq0kjOYtj0HUMWyeJ2kUIdA7NPYJ\nSfsBy8xsGCHQ6lrOfdiVEDR8I2lPwnqhlIbd4kL5ywnTX7Cl31xSt+JUbSHBtgVC8NkuBul9EtUr\n41P3GGHkqlncn0ZYn7V7/J6cBbwWj61LfdcibxKmL78ysw3xu9+EML2XWmj+T+AMSfXjmq3uZJhm\nVWb/wAVAM0lHxDo7SOqQfq5TM5x11lkcccQRLFiwgNNPP52HH36YYcOGceWVV9KlSxeuu+46Hnzw\nQQAuvvhiVq1aRYcOHTj00EM599xz6dy5PKtFx3HylW01ju5ecZTrFdcQGB5/qOsRpi1T01q3A89I\nOp8wNffrMpq/MwYGRghUL4zlewAvK/jQfUIw200yMd6reoQA6b9j+ZPAC5JKCNOCqcCOuNZnIvB1\nnKKCsCD+agVfuVWE9Wpl3YfZkt6JbS5l07RXY8L3YifC6F0qXcDfgGExYEqNJN4VP7tdgCmEUa8f\n47GBhBG5L6L21PRdhT51sW/3EtZTEUcCBwIT2bTYPLUO60HCGsCZZnYOYTpud8IUYYrUNG9qRG4U\nIbCaTfisrjGzzyW1TbtNnUjzD4zaTgPulbRb7MMQwmJ3p4YZMWJE6XZyCmLGjBlb1G3UqBEjR47c\nVtIcx6lF3GvPqTJxpGcmcLqZLaxtPU75uNde9nGtNYNrzT75ohNyS6vca8+pKRQSY35AyG3lQZTj\nOI6zXZN7j8g4OU1cX9WqtnU4juM4Ti7gI1KO4+Q1mTzwrr76atq2bUvnzp3p06cPX3/9NeAeeI7j\nZB/32ssBr714vSaSnpU0X9J7iSezKuu1VyRpStQ2PaYRcK+9yrXvXnt5TCYPvJ49ezJ37lzmzJnD\ngQceWBowuQee4zjZxr32csNrD8JTYuPMrC3hsf/3YnllvfbuBG6OWm+I++Bee5XFvfbylEweeMcf\nfzwFBeE2Hn744Xz88ceAe+A5jpN93GuP2vfai4+ydyckuSQ+xp96lL+yXnsW7wWEHFCfxm332nOv\nvTrptVeeB16SRx55hDPOOANwDzzHcbKPe+1FrBa99giZr78AHpXUJWobYGarqbzX3mWEfFF/Iow0\nHhnL3WtvE9uN154TuPXWWykoKOCcc84BYNq0adSvX59PP/2UlStXctRRR9GjRw9atfLnJxzHqR7u\ntZddquu1N5vwI3qpmU2VdA9hCu+/ko3H4KKsxF+/By43s+ck/Rp4mJCh2732NrHdeO1pOzAtzmQm\nnCwbN24cL7zwAoMGDeK110Iy+iFDhtC+fXsmTw4fR6tWrRg+fDjHHJP8p6Vi8slc1bXWDPmiNV90\nQn5pTeJee5vq1JrXXlyT9bGZTY1Fz7JpLVSlvPYIoygD4jkjCTYt7rW3nXrtWcK0uE2bNnbpOb2r\n2JVtz6RJk/h1NZPxpcyEU8n8xo0bx9ixY3nttddo1qxZab2pU6cyf/58iouLWb16NUuWLOGOO+6o\nsn1LLiUOrAjXWjPki9Z80Qn5pTWJe+1R+157ZvY5YW1Sm1jvOEKwB5X02iOsiTo6bh8LLIzXcK+9\nTbjXXh0k6YHXokULHn74YS655BK+++47evbsSVFRERddFJyA3APPcZxs4157ueG1t5zwA/+kwpNn\nHwHnxtMq67V3AXCPwhNf3xOndXCvPffaq+MkPfBSnH/++Rlqugee4zjZx732nCoj99rLK9xrL/u4\n1prBtWaffNEJuaVV7rXn1BRyrz3HcRzHKcUT/zlVwtxrz3Ecx3FK8REpx3G2ipYtW9KpUyeKioo4\n5JAwEj5y5Eg6dOhAvXr1mD59ei0rdBzHqTk8kHJKUd3xR7xcWfbj21ok3STpqmqe21LS2dnWlE0m\nTpzIrFmzSoOmjh078vzzz9O9e/daVuY4jlOzeCDlJKkr/ogpq5Zs+/HVFi2BnA6k0mnXrh1t2rSp\nuKLjOE6e42uknCR1xh8xqb0MP77jo+4dgQ+Bc6Pf4OLYh18QsvGfbWYfqAyfvjhity9h3di+wBAz\nuzfeo+sJub+WE9I8zIjlBxDsk5oRfPguMLP58R5/S/BC3IuQGuHZeH/axb4Nj319lJDIth7wq/IW\n/teE117S504SPXr0oH79+lx44YX87ne/K+dMx3GcuoUHUk46dcIfMebdSmcmwRB4MsGqpoeZrZZ0\nLSFn1S2x3jdm1ikmbB1CSLJZnk9fW4J9UWNggYIhc2eCD2ER4e9sZkL/g4TcZwsl/YyQCDWVwb2Q\nYCPTlpCMNZXl/ioz6xX7dh9wj5ml8o7Vz/ShbCveeOMNmjdvzvLly+nZsydt27b1KT3HcbYbPJBy\nNqOO+yOmTPYOJ3j7TY6JURsQTIZTjEi8p6YJy/Pp+7uZ/QD8IGk5wYPwKGBUKlO+gvFyKnv7kcDI\nRFs7Jq49Ot6jeTHreybeIngCtgCezzQapRr22kv3w1q4MEg46KCDGDFiBBs3bgTg66+/ZsaMGaxa\ntarCNvPJZ8u11gyuNfvki07IL61JPJByMlFX/RFTfnwCxpvZWWXUswzb5fn0Jfu/gfL/ruoRMsJn\nsqdJbyujEbOZPSVpKsHX8CVJF5rZhLQ628Rrb/Xq1WzcuJHGjRuzevVqrrvuOm644YbSpHpNmjTh\n4IMPLn2arzxyKRlfRbjWmsG1Zp980Qn5pTWJLzZ3tqCu+SMqkPTjmwL8PE4tIqmhpAMTp5yReE+N\nVFXGpy/J68ApknZWMIP+VwgjfoR1XacntHUppx1I8xqU1Ar4KK7FGkOYRqwVli1bRrdu3ejSpQuH\nHXYYJ510EieeeCKjRo2iRYsWvPXWW5x00kmccMIJtSXRcRynRvERKacs6oI/Yll+fF9I6g+MiOuy\nIKyZej9u/0TBj+8HgkkxVMKnL03vTElPEzz1lgNvJw6fAzwg6Q+EgPRvsV5ZzAE2xHv/GGEqsK+C\nr+HnwP+Uc26N0qpVK2bP3lJ6nz596NOnT4YzHMdx6hYeSDmlmFmjxPYyQgCS3D88Uf3aWD4JmJSo\nV5zYLj1mZv3LuOZGQjCUHhBt1m6su5Q4spOhncVAx8R+xusljk8ADi3j8F1mdm1a/RVsGqlKlt+U\ntp/UcCsxPUNanUXAiRnK+6ftN4rv69i0GD3F7WVodxzHcbYhPrXnOI7jOI5TTXxEynESmFnL2tbg\nOI7j5A8+IuU4juM4jlNNPJBynO2EpUuXcswxx9C+fXs6dOjAPffcU9uSHMdx8h4PpJwqE82N/5rY\nL5D0haQXq9leE0n/ntgvLqstSZMklZuUSFLF2R9rmWhivEbSHomyCnUrYf5cmXuRpKCggEGDBjFv\n3jymTJnC0KFDmTdvXvU64DiO4wAeSDnVYzXQUdLOcb8n8MlWtNcE+PcKa9U9VgBXbquLFRYW0rVr\nVwAaN25Mu3bt+OSTrfnYHMdxHA+knOryEiGzNoRcSylbFSQ1lTRa0hxJUyR1juU3SXokjqR8RsY7\n6gAAChZJREFUFJNkQniU/wBJsyTdFcsaSXpW0nxJTyrhpxLbOk/SkMT+BZIGp9Upjtfaoh1Jh0p6\nU9JsSdMkNZa0k6RHJZVIekfSMbFu/9if8ZIWS7pE0hWxzhRJTWO9AySNkzRD0j8lta3gHj4CnJE6\nP6G7paS5if2rFMyRs8bixYt55513+NnPfpbNZh3HcbY7/Kk9p7r8DbghTsF1JgQFR8VjNwPvmNkp\nko4FHieY90Jmg9+BBHPilOFwMcHOpQPwKTAZ+DnwRuL6zxD85q6OeZbOBS7MoHOLdiRNA54GzjCz\nt6PVzFpgACH3Z6cYBL2SyHjeMba1E8GS5lozOygGbylz4/LMiDOxKt63AcCN5dSrMkp47TVr1mwz\n/6q1a9cyYMAAfvvb3zJz5sxsXnaryCefLddaM7jW7JMvOiG/tCbxQMqpFmY2R1JLwmjUS2mHuwG/\nivUmSPppDFYgs8FvJqaZ2ccAkmYRsp+XBlJmtkrSBKCXpPeAHcyspJLtfAN8ZmZvx7a+jce7AffF\nsvmSlgCpQGqimX0HfCfpG+CFWF5CMF2uyIy4LO4FZkn6UyXqVpp0r72Uf9W6devo1asXF110EVdc\ncUU2L7nV5JPPlmutGVxr9skXnZBfWpN4IOVsDWOBPwHFwE8reU5lDX4rU+8hQkb0+WxusFyd61VE\nRcbMFZkRZ8TMvpb0FHBxorjUIDqyU9XlZrwW559/Pu3atcu5IMpxHCdf8TVSztbwCHBzhpGgfxL8\n5FLTdCtSoz5lsJkpb2Uxs6nAPsDZJNZoVYIFQKGkQ6PGxpIK0nQfCOwb61ZGS3XMiFPcTZiWTAV5\ny4A94kjejkCvSrZTLpMnT+aJJ55gwoQJFBUVUVRUxEsvpQ8mOo7jOFXBR6ScahOnzO7NcOgm4JFo\n8LsG6FdBO19KmhwXWP8/4O9VkPEMUGRmKyt7gpn9KOkM4L745OFaoAdhTdMDkkoIo0L9zeyHtHXu\n5VFVM+KUnhWSRgGXx/11km4hGDx/Qhhx22q6deuGmWWjKcdxHCfigZRTZZLmxomySWwyKP4KOCVD\nnZvS9pMGv2enVZ+UOHZJYrs4rV43YLOn9RJmv6WaMrTzNpubMKc4N4Pux4DHEvstMx0ry4w4Exnu\nxRXAFYn9e8kQpCaNjTPcC8dxHGcb41N7Tl4Sk3i+D6w1s1drW4/jOI6zfeIjUk5eYmZfs+mJupxF\n0vXA6WnFI83s1trQ4ziO42QXD6QcpwaJAZMHTY7jOHUUn9pzHMdxHMepJh5IOY7jOI7jVBMPpBzH\ncRzHcaqJPK+M49RtJH1HJROL1jK7AytqW0Qlca01g2vNPvmiE3JL635m1qwyFX2xuePUfRaY2SG1\nLaIiJE3PB53gWmsK15p98kUn5JfWJD615ziO4ziOU008kHIcx3Ecx6kmHkg5Tt3nwdoWUEnyRSe4\n1prCtWaffNEJ+aW1FF9s7jiO4ziOU018RMpxHMdxHKeaeCDlOI7jOI5TTTyQcpw6iqQTJS2Q9IGk\ngTmg5xFJyyXNTZQ1lTRe0sL4/pPEsf+M2hdIOmEb6txH0kRJ8yS9K2lADmvdSdI0SbOj1ptzVWvi\n+vUlvSPpxVzWKmmxpBJJsyRNz3GtTSQ9K2m+pPckHZFrWiW1ifcy9fpW0mW5prNamJm//OWvOvYC\n6gMfAq2ABsBsoH0ta+oOdAXmJsruBAbG7YHAHXG7fdS8I7B/7Ev9baSzEOgatxsD70c9uahVQKO4\nvQMwFTg8F7UmNF8BPAW8mKvfgXj9xcDuaWW5qnU48Nu43QBokqtao4b6wOfAfrmss7IvH5FynLrJ\nYcAHZvaRmf0I/A3oXZuCzOx14Ku04t6EHwHi+ymJ8r+Z2Q9mtgj4gNCnbaHzMzObGbe/A94Dmueo\nVjOzVXF3h/iyXNQKIKkFcBLwUKI4J7WWQc5plbQb4T8pDwOY2Y9m9nUuak1wHPChmS3JcZ2VwgMp\nx6mbNAeWJvY/jmW5xp5m9lnc/hzYM27nhH5JLYGDCCM9Oak1TpXNApYD480sZ7UCQ4BrgI2JslzV\nasA/JM2Q9LtYlota9we+AB6NU6YPSWqYo1pTnAmMiNu5rLNSeCDlOE5OYGE8P2fysUhqBDwHXGZm\n3yaP5ZJWM9tgZkVAC+AwSR3TjueEVkm9gOVmNqOsOrmiNdIt3tdfABdL6p48mENaCwhT5g+Y2UHA\nasIUWSk5pBVJDYCTgZHpx3JJZ1XwQMpx6iafAPsk9lvEslxjmaRCgPi+PJbXqn5JOxCCqCfN7Plc\n1poiTudMBE4kN7X+HDhZ0mLCVPOxkv6ao1oxs0/i+3JgFGFaKRe1fgx8HEciAZ4lBFa5qBVCYDrT\nzJbF/VzVWWk8kHKcusnbQGtJ+8f/AZ4JjK1lTZkYC/SL2/2AMYnyMyXtKGl/oDUwbVsIkiTCepP3\nzOzuHNfaTFKTuL0z0BOYn4tazew/zayFmbUkfB8nmNm/5aJWSQ0lNU5tA8cDc3NRq5l9DiyV1CYW\nHQfMy0WtkbPYNK2X0pOLOitPba9295e//FUzL+CXhCfOPgSuzwE9I4DPgHWE/0WfD/wUeBVYCPwD\naJqof33UvgD4xTbU2Y0wvTAHmBVfv8xRrZ2Bd6LWucANsTzntKbpLmbTU3s5p5XwtOvs+Ho39feT\ni1rjtYuA6fF7MBr4SS5qBRoCXwK7JcpyTmdVX24R4ziO4ziOU018as9xHMdxHKeaeCDlOI7jOI5T\nTTyQchzHcRzHqSYeSDmO4ziO41QTD6Qcx3Ecx3GqSUFtC3Acx3HyD0kbgJJE0SlmtriW5DhOreHp\nDxzHcZwqI2mVmTXahtcrMLP12+p6jlNZfGrPcRzHyTqSCiW9LmmWpLmSjorlJ0qaKWm2pFdjWVNJ\noyXNkTRFUudYfpOkJyRNBp6IBs13SXo71r2wFrvoOIBP7TmO4zjVY2dJs+L2IjPrk3b8bOBlM7tV\nUn1gF0nNgGFAdzNbJKlprHsz8I6ZnSLpWOBxQrZugPYEA+G1kn4HfGNmh0raEZgs6RUzW1STHXWc\n8vBAynEcx6kOa82sqJzjbwOPRAPo0WY2S1Ix8Hoq8DGzr2LdbsCvYtkEST+VtGs8NtbM1sbt44HO\nkk6L+7sRPNg8kHJqDQ+kHMdxnKxjZq9L6g6cBDwm6W5gZTWaWp3YFnCpmb2cDY2Okw18jZTjOI6T\ndSTtBywzs2HAQ0BXYArQXdL+sU5qau+fwDmxrBhYYWbfZmj2ZeD3cZQLSQdKalijHXGcCvARKcdx\nHKcmKAaulrQOWAX8xsy+iOucnpdUD1gO9ARuIkwDzgHWAP3KaPMhoCUwU5KAL4BTarITjlMRnv7A\ncRzHcRynmvjUnuM4juM4TjXxQMpxHMdxHKeaeCDlOI7jOI5TTTyQchzHcRzHqSYeSDmO4ziO41QT\nD6Qcx3Ecx3GqiQdSjuM4juM41eT/A939xO/rzKLJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x28454a6be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_iteration =[]\n",
    "best_score= []\n",
    "training_score = []\n",
    "for train_ind,val_ind in skf.split(train_X,train_y):\n",
    "    #print (set(train_y))\n",
    "    #print (type(train_y))\n",
    "    X_train,X_val = train_X.iloc[train_ind,],train_X.iloc[val_ind,]\n",
    "    y_train,y_val = train_y.iloc[train_ind],train_y.iloc[val_ind]\n",
    "    #print (set(train_y))\n",
    "    #print (max(train_ind),min(train_ind),max(val_ind),min(val_ind))\n",
    "    #print (train_ind,val_ind)\n",
    "    #print(set(y_train))\n",
    "    dtrain = xgb.DMatrix(X_train,y_train,feature_names = X_train.columns)\n",
    "    dval = xgb.DMatrix(X_val,y_val,feature_names = X_val.columns)\n",
    "    model = xgb.train(xgb_params,dtrain,num_boost_round=1000,\n",
    "                      evals=[(dtrain,'train'),(dval,'val')],verbose_eval=True,early_stopping_rounds=30)\n",
    "    best_iteration.append(model.attributes()['best_iteration'])\n",
    "    best_score.append(model.attributes()['best_score'])\n",
    "    # training_score.append(model.attributes()['best_msg'].split()[1][-8:])\n",
    "    xgb.plot_importance(model)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the feature importance plot, we can see that the newly created features: \"MonthlyIncome_Null\" and \"NoD_Null\" is not as predictive as I thought. I am going to drop them off at this point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These features have been dropped\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    train_X.drop(['MonthlyIncome_Null','NoD_Null'],axis=1,inplace=True)\n",
    "    train_df.drop(['MonthlyIncome_Null','NoD_Null'],axis=1,inplace=True)\n",
    "except ValueError:\n",
    "    print (\"These features have been dropped\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a function to perform cross validation in order to tune key parameters: 'eta','max_depth','sub_sample','colsample_bytree'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameters are (0.04, 4, 0.9, 0.5). Training performance is 0.8771. Validation performance is 0.8651\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({(0.04, 4, 0.9, 0.5): 0.8651120000000001},\n",
       " {(0.04, 4, 0.9, 0.5): 0.87709499999999996})"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def xgbCV(eta=[0.05],max_depth=[6],sub_sample=[0.9],colsample_bytree=[0.9]):\n",
    "    train_y = train_df['SeriousDlqin2yrs'] # label for training data\n",
    "    train_X = train_df.drop(['SeriousDlqin2yrs','ID'],axis=1,inplace=False) # feature for training data\n",
    "    test_X = test_df.drop(['SeriousDlqin2yrs','ID'],axis=1,inplace=False) # feature for testing data\n",
    "    skf = model_selection.StratifiedKFold(n_splits=5,random_state=100) # stratified sampling\n",
    "    train_performance ={} \n",
    "    val_performance={}\n",
    "    for each_param in itertools.product(eta,max_depth,sub_sample,colsample_bytree): # iterative over each combination in parameter space\n",
    "        xgb_params = {\n",
    "                    'eta':each_param[0],\n",
    "                    'max_depth':each_param[1],\n",
    "                    'sub_sample':each_param[2],\n",
    "                    'colsample_bytree':each_param[3],\n",
    "                    'objective':'binary:logistic',\n",
    "                    'eval_metric':'auc',\n",
    "                    'silent':0\n",
    "                    }\n",
    "        best_iteration =[]\n",
    "        best_score=[]\n",
    "        training_score=[]\n",
    "        for train_ind,val_ind in skf.split(train_X,train_y): # five fold stratified cross validation\n",
    "            X_train,X_val = train_X.iloc[train_ind,],train_X.iloc[val_ind,] # train X and train y\n",
    "            y_train,y_val = train_y.iloc[train_ind],train_y.iloc[val_ind] # validation X and validation y\n",
    "            dtrain = xgb.DMatrix(X_train,y_train,feature_names = X_train.columns) # convert into DMatrix (xgb library data structure)\n",
    "            dval = xgb.DMatrix(X_val,y_val,feature_names = X_val.columns) # convert into DMatrix (xgb library data structure)\n",
    "            model = xgb.train(xgb_params,dtrain,num_boost_round=1000, \n",
    "                              evals=[(dtrain,'train'),(dval,'val')],verbose_eval=False,early_stopping_rounds=30) # train the model\n",
    "            best_iteration.append(model.attributes()['best_iteration']) # best iteration regarding AUC in valid set\n",
    "            best_score.append(model.attributes()['best_score']) # best score regarding AUC in valid set\n",
    "            training_score.append(model.attributes()['best_msg'].split()[1][10:]) # best score regarding AUC in training set\n",
    "        valid_mean = (np.asarray(best_score).astype(np.float).mean()) # mean AUC in valid set\n",
    "        train_mean = (np.asarray(training_score).astype(np.float).mean()) # mean AUC in training set\n",
    "        val_performance[each_param] =  train_mean\n",
    "        train_performance[each_param] =  valid_mean\n",
    "        print (\"Parameters are {}. Training performance is {:.4f}. Validation performance is {:.4f}\".format(each_param,train_mean,valid_mean))\n",
    "    return (train_performance,val_performance)\n",
    "#xgbCV(eta=[0.01,0.02,0.03,0.04,0.05],max_depth=[4,6,8,10],colsample_bytree=[0.3,0.5,0.7,0.9]) \n",
    "xgbCV(eta=[0.04],max_depth=[4],colsample_bytree=[0.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['RevolvingUtilizationOfUnsecuredLines', 'age',\n",
      "       'NumberOfTime30-59DaysPastDueNotWorse', 'DebtRatio', 'MonthlyIncome',\n",
      "       'NumberOfOpenCreditLinesAndLoans', 'NumberOfTimes90DaysLate',\n",
      "       'NumberRealEstateLoansOrLines', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
      "       'NumberOfDependents', 'IncomePerPerson', 'NumOfPastDue', 'MonthlyDebt',\n",
      "       'NumOfOpenCreditLines', 'MonthlyBalance', 'age_sqr'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train_X.columns)\n",
    "any(train_X.columns == test_X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = xgb.DMatrix(train_X,train_y,feature_names=train_X.columns)\n",
    "test = xgb.DMatrix(test_X,feature_names=test_X.columns)\n",
    "xgb_params = {\n",
    "                    'eta':0.03,\n",
    "                    'max_depth':4,\n",
    "                    'sub_sample':0.9,\n",
    "                    'colsample_bytree':0.5,\n",
    "                    'objective':'binary:logistic',\n",
    "                    'eval_metric':'auc',\n",
    "                    'silent':0\n",
    "                    }\n",
    "\n",
    "final_model = xgb.train(xgb_params,train,num_boost_round=500)\n",
    "ypred = final_model.predict(test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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I/c6blHbu3LkAHHroobzwwgvssssuNG7cmPfff5+GDRuybt06Ro0axe677w7A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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8f6e7eea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(final_model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot a tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#xgb.to_graphviz(final_model,num_trees=0,size='20,20')\n",
    "#plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "yout = pd.DataFrame({'Id':test_df.ID.values,'Probability':ypred})\n",
    "yout.to_csv('E:\\\\Kaggle\\\\Give Me Some Credit\\\\result\\\\xgboost_result.csv',index=False)"
   ]
  }
 ],
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